Personalized automatic content aggregation generation

ABSTRACT

Generating personalized aggregated content is disclosed herein. The system can include a memory include an aggregated content database. The system can include a user device having a first network interface and a first I/O subsystem. The system can include one or more servers that can include a packet selection system and a presentation system. These one or more servers can: receive content files from the user device. A server can parse the content files and further generate features and feature vectors based on a related domain model. Content from the parsed content files may then be used to generate cards or content aggregations.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent application Ser. No. 15/606,832, filed on May 26, 2017, and entitled “PERSONALIZED AUTOMATIC CONTENT AGGREGATION GENERATION”; and the present application is a continuation-in-part of U.S. patent application Ser. No. 15/236,275, filed on Aug. 12, 2016, and entitled “SYSTEM AND METHOD FOR AUTOMATIC CONTENT AGGREGATION GENERATION”, which claims the benefit of U.S. Provisional Application No. 62/320,213, filed on Apr. 8, 2016, and entitled “ADAPTIVE PATHWAYS AND COGNITIVE TUTORING”, the entirety of each of which are hereby incorporated by reference herein.

BACKGROUND

A computer network or data network is a telecommunications network which allows computers to exchange data. In computer networks, networked computing devices exchange data with each other along network links (data connections). The connections between nodes are established using either cable media or wireless media. The best-known computer network is the Internet.

Network computer devices that originate, route and terminate the data are called network nodes. Nodes can include hosts such as personal computers, phones, servers as well as networking hardware. Two such devices can be said to be networked together when one device is able to exchange information with the other device, whether or not they have a direct connection to each other.

Computer networks differ in the transmission media used to carry their signals, the communications protocols to organize network traffic, the network's size, topology and organizational intent. In most cases, communications protocols are layered on other more specific or more general communications protocols, except for the physical layer that directly deals with the transmission media. There is a need to deliver automatically generated and automatically aggregated content to a user device over a network at frequencies and intervals associated with learning the aggregated content.

BRIEF SUMMARY

One general aspect includes a processor-based system for automatically generating personalized aggregated content, the processor-based system including: a communication network. The processor-based system also includes a memory including a first content aggregation and at least a user profile data base. The first content aggregation including at least one first content aggregation having one or more first text strings extracted from one or more first content files, the one or more first content files being relevant to a common subject, and the one or more first sentences selected for extraction from the one or more first content files via a topic model associated with the common subject. The processor-based system also includes at least one server, including one or more processors, configured to: receive, from a user device, one or more second content files via a first electrical signal communicated via the communication network; process each second content file of the one or more second content files by automatically extracting one or more second sentences, from each second content file; generate a second content aggregation by placing, for each second content file, the one or more second sentences in the second content aggregation; associate the second content aggregation with a user of the user device; store the second content aggregation in the user profile database; and transmit, to the user device, the second content aggregation via a second electrical signal communicated via the communication network.

Implementations may include one or more of the following features. The server may be configured to: determine whether the second content aggregation is sufficient and to transmit, to the user device, the first content aggregation via the communication network when the second content aggregation is not sufficient. The server may be configured to determine whether the second content aggregation meets a threshold value and to transmit, to the user device, the first content aggregation via the communication network when the second content aggregation is less than the threshold value. The threshold value may be configured to be received from the user device. The first content aggregation may include at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and the server further configured to: apply the topic model to the one or more second text strings to determine which one of the at least two first content aggregations to transmit to the user device. The first content aggregation may include at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and the server further configured to: apply the topic model to the one or more second content files to determine whether to transmit one or both of the at least two first content aggregations to the user device. The first content aggregation may include at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and the server further configured to: search the first content aggregations and transmit the first content aggregations that include two or more words present in the one or more second content files. The one or more first content files may include written material. The user may create the one or more second content files. The one or more content files may include audio files, text documents, scanned documents, word processor generated documents, PDF generated documents, and/or presentation processing created documents.

In another general aspect, a method for automatically generating personalized aggregated content is provided that includes storing in a memory a first content aggregation, the first content aggregation including one or more first text strings extracted from one or more first content files. The one or more first content files being relevant to a common subject, and the one or more first text strings selected for extraction from the one or more first content files via a topic model associated with the common subject. The method includes receiving, at a server and from a user device, one or more second content files via a first electrical signal communicated via a communication network; processing each second content file of the one or more second content files by automatically extracting: one or more second text strings, from each second content file; generating a second content aggregation by placing, for each second content file, the one or more second text strings in the second content aggregation; associating the second content aggregation with a user of the user device; storing the second content aggregation in the user profile database; and transmitting, to the user device, the second content aggregation via a second electrical signal communicated via the communication network.

Implementations may include one or more of the following features. The method may further include determining whether the second content aggregation is sufficient and transmitting, to the user device, the first content aggregation via the communication network when the second content aggregation is not sufficient. The method may include determining whether the second content aggregation meets a threshold value and transmitting, to the user device, the first content aggregation via the communication network when the second content aggregation is less than the threshold value. The threshold value may be configured to be received from the user device. The first content aggregation may include at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and further including: applying the topic model to the one or more second text strings to determine which one of the at least two first content aggregations to transmit to the user device. The first content aggregation may include at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and further comprising: applying the topic model to the one or more second content files to determine whether to transmit one or both of the at least two first content aggregations to the user device. The first content aggregation may include at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and further comprising: searching the first content aggregations and transmitting the first content aggregations that include two or more words present in the one or more second content files. The one or more first content files may include written material. The user may create the one or more second content files. The one or more second content files may include audio files, text documents, scanned documents, word processor generated documents, PDF generated documents, and/or presentation processing created documents.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example of a content distribution network.

FIG. 2 is a block diagram illustrating a computer server and computing environment within a content distribution network.

FIG. 3 is a block diagram illustrating an embodiment of one or more data store servers within a content distribution network.

FIG. 4 is a block diagram illustrating an embodiment of one or more content management servers within a content distribution network.

FIG. 5 is a block diagram illustrating the physical and logical components of a special-purpose computer device within a content distribution network.

FIG. 6 is a block diagram illustrating one embodiment of the communication network.

FIG. 7 is a block diagram illustrating one embodiment of the user device and supervisor device communication.

FIG. 8 is a schematic illustration of one embodiment of a computing stack.

FIG. 9A-9C are schematic illustrations of embodiments of communication and processing flow of modules within the content distribution network.

FIG. 10A is a flowchart illustrating one embodiment of a process for data management.

FIG. 10B is a flowchart illustrating one embodiment of a process for evaluating a response.

FIG. 11 is a flowchart illustrating one embodiment of a process for automated aggregation delivery.

FIG. 12 is a flowchart illustrating one embodiment of a process for decay based content provisioning.

FIG. 13 is a flowchart illustrating one embodiment of a process for automated content aggregation generation.

FIG. 14 is a flowchart illustrating one embodiment of a process for automated identification of noun phrases in a parse tree.

FIG. 15 is a flowchart illustrating one embodiment of a process for automated creation of a subset of rational phrases.

FIG. 16 is a flowchart illustrating one embodiment of a detailed process for automated content aggregation generation.

FIG. 17 is a flowchart illustrating one embodiment of a process for automated content aggregation evaluation.

FIG. 18 is a flowchart illustrating one embodiment of a process for automated evaluation of a set of content aggregations.

FIG. 19 is a flowchart illustrating one embodiment of a process for automated personalized content aggregation.

FIG. 20 is a flowchart illustrating one embodiment for generating multi-choice personalized content aggregation.

FIG. 21 is a flowchart illustrating one embodiment for presenting personalized content aggregation to a user.

FIG. 22 is a pictorial illustration of the movement of incomplete clauses between buckets.

FIG. 23 is a flowchart illustrating one embodiment for ordering and presenting personalized aggregated content to a user.

FIG. 24 is a flowchart illustrating one embodiment of a process for automated personalized content aggregation.

FIG. 25 is a flowchart illustrating one embodiment for identifying or determining a hierarchy of content segments or text strings that may be used for automated personalized content aggregation.

FIG. 26 is a flowchart illustrating one embodiment for evaluating and presenting personalized content aggregation to a user.

In the appended figures, similar components and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label by a dash and a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and is not intended to limit the scope, applicability or configuration of the disclosure. Rather, the ensuing description of the illustrative embodiment(s) will provide those skilled in the art with an enabling description for implementing a preferred exemplary embodiment. It is understood that various changes can be made in the function and arrangement of elements without departing from the spirit and scope as set forth in the appended claims.

This application is related to U.S. application Ser. No. 15/236,196, filed on Aug. 12, 2016, and entitled SYSTEMS AND METHODS OF EVENT-BASED CONTENT PROVISIONING; U.S. application Ser. No. 15/236,103, filed on Aug. 12, 2016, and entitled SYSTEMS AND METHODS FOR DATA PACKET METADATA STABILIZATION; and U.S. application Ser. No. 15/236,275, filed on Aug. 12, 2016, and entitled SYSTEM AND METHOD FOR AUTOMATIC CONTENT AGGREGATION GENERATION, the entirety of each of which is hereby incorporated by reference herein.

Embodiments discussed in the present specification describe automatically generating personalized content that is aimed at enabling a user (such as a student) to prepare for a challenge—such as an exam, assessment, final, or evaluation. The personalized content may be generated from content files provided by the user. Content files may include text in documents provided by a third party (such as an instructor) including lecture material provided in a document or presentation format, books or articles in electronic format, quizzes, and/or past exams. Personalized content may also be generated from blogs, outlines, notes, and/or comments and other information sources found using a network such as the internet. The personalized content may also be generated from text in material authored by the user such as course notes and/or outlines in electronic format. The personalized content may also be created when the user highlights a portion of text and/or images in an electronic document. The personalized content may be transformed into a data packet (an independent clause) containing at a least four words including at least a noun and a verb. A data packet may take many forms including flashcard material. The data packet may be further processed to extract one or more words from the data packet to form an incomplete clause. The content files may be further processed to generate a plurality of words to replace the extracted one or more words that will be transmitted to the user for selection of a response. The personal content may also be generated from images any of the documents discussed previously. In this embodiment the present specification describes the subsequent generation of aggregated content that includes data packets aggregated together that are transmitted to the user on the user device and each data packet requiring a user response. The data packets may be initially transmitted in an order specifically generated in order of difficulty. For example a less difficult data packet may be one that is missing words (extractions or noun phrases) but subsequently presents the words and may require a user response indicating that the user either knew or did not know the missing words. Additionally, a more difficult data packet may subsequently give the user a choice of correct responses. Finally, a most difficult data packet may subsequently require the user to create the missing words for the response. The present specification also describes receiving the user response from a user device and comparing the response to a baseline response. Based on the comparison the data packets may be re-aggregated such that data packets with desired responses are not transmitted to the user device as frequently as data packets with undesired responses. Furthermore, when a user is just beginning preparation for a challenge, the data packets presented first to the user may be less difficult data packets and the difficulty will progressively increase as the user gets closer in time to the challenge and/or as the user exhibits desired responses to the data packets. The timing and order of presentation of data packets to users enables the user to better prepare for challenges. Furthermore—over time the presentation order and frequency may be adapted according to multiple user responses and results in order to improve the user outcome.

With reference now to FIG. 1, a block diagram is shown illustrating various components of a content distribution network (CDN) 100 which implements and supports certain embodiments and features described herein. In some embodiments, the content distribution network 100 can comprise one or several physical components and/or one or several virtual components such as, for example, one or several cloud computing components. In some embodiments, the content distribution network 100 can comprise a mixture of physical and cloud computing components.

Content distribution network 100 may include one or more content management servers 102. As discussed below in more detail, content management servers 102 may be any desired type of server including, for example, a rack server, a tower server, a miniature server, a blade server, a mini rack server, a mobile server, an ultra-dense server, a super server, or the like, and may include various hardware components, for example, a motherboard, a processing unit, memory systems, hard drives, network interfaces, power supplies, etc. Content management server 102 may include one or more server farms, clusters, or any other appropriate arrangement and/or combination of computer servers. Content management server 102 may act according to stored instructions located in a memory subsystem of the server 102, and may run an operating system, including any commercially available server operating system and/or any other operating systems discussed herein.

The content distribution network 100 may include one or more data store servers 104, such as database servers and file-based storage systems. The database servers 104 can access data that can be stored on a variety of hardware components. These hardware components can include, for example, components forming tier 0 storage, components forming tier 1 storage, components forming tier 2 storage, and/or any other tier of storage. In some embodiments, tier 0 storage refers to storage that is the fastest tier of storage in the database server 104, and particularly, the tier 0 storage is the fastest storage that is not RAM or cache memory. In some embodiments, the tier 0 memory can be embodied in solid state memory such as, for example, a solid-state drive (SSD) and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one or several higher performing systems in the memory management system, and that is relatively slower than tier 0 memory, and relatively faster than other tiers of memory. The tier 1 memory can be one or several hard disks that can be, for example, high-performance hard disks. These hard disks can be one or both of physically or communicatingly connected such as, for example, by one or several fiber channels. In some embodiments, the one or several disks can be arranged into a disk storage system, and specifically can be arranged into an enterprise class disk storage system. The disk storage system can include any desired level of redundancy to protect data stored therein, and in one embodiment, the disk storage system can be made with grid architecture that creates parallelism for uniform allocation of system resources and balanced data distribution.

In some embodiments, the tier 2 storage refers to storage that includes one or several relatively lower performing systems in the memory management system, as compared to the tier 1 and tier 2 storages. Thus, tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier 2 memory can include one or several SATA-drives or one or several NL-SATA drives.

In some embodiments, the one or several hardware and/or software components of the database server 104 can be arranged into one or several storage area networks (SAN), which one or several storage area networks can be one or several dedicated networks that provide access to data storage, and particularly that provide access to consolidated, block level data storage. A SAN typically has its own network of storage devices that are generally not accessible through the local area network (LAN) by other devices. The SAN allows access to these devices in a manner such that these devices appear to be locally attached to the user device.

Data stores 104 may comprise stored data relevant to the functions of the content distribution network 100. Illustrative examples of data stores 104 that may be maintained in certain embodiments of the content distribution network 100 are described below in reference to FIG. 3. In some embodiments, multiple data stores may reside on a single server 104, either using the same storage components of server 104 or using different physical storage components to assure data security and integrity between data stores. In other embodiments, each data store may have a separate dedicated data store server 104.

Content distribution network 100 also may include one or more user devices 106 and/or supervisor (instructors in some embodiments) devices 110. User devices 106 and supervisor devices 110 may display content received via the content distribution network 100, and may support various types of user interactions with the content. User devices 106 and supervisor devices 110 may include mobile devices such as smartphones, tablet computers, personal digital assistants, and wearable computing devices. Such mobile devices may run a variety of mobile operating systems, and may be enabled for Internet, e-mail, short message service (SMS), Bluetooth®, mobile radio-frequency identification (M-RFID), and/or other communication protocols. Other user devices 106 and supervisor devices 110 may be general purpose personal computers or special-purpose computing devices including, by way of example, personal computers, laptop computers, workstation computers, projection devices, and interactive room display systems. Additionally, user devices 106 and supervisor devices 110 may be any other electronic devices, such as thin-client computers, Internet-enabled gaming systems, business or home appliances, and/or personal messaging devices, capable of communicating over network(s) 120.

In different contexts of content distribution networks 100, user devices 106 and supervisor devices 110 may correspond to different types of specialized devices, for example, student devices and teacher devices in an educational network, employee devices and presentation devices in a company network, different gaming devices in a gaming network, etc. In some embodiments, user devices 106 and supervisor devices 110 may operate in the same physical location 107, such as a classroom or conference room. In such cases, the devices may contain components that support direct communications with other nearby devices, such as a wireless transceivers and wireless communications interfaces, Ethernet sockets or other Local Area Network (LAN) interfaces, etc. In other implementations, the user devices 106 and supervisor devices 110 need not be used at the same location 107, but may be used in remote geographic locations in which each user device 106 and supervisor device 110 may use security features and/or specialized hardware (e.g., hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.) to communicate with the content management server 102 and/or other remotely located user devices 106. Additionally, different user devices 106 and supervisor devices 110 may be assigned different designated roles, such as presenter devices, teacher devices, administrator devices, or the like, and in such cases the different devices may be provided with additional hardware and/or software components to provide content and support user capabilities not available to the other devices.

The content distribution network 100 also may include a privacy server 108 that maintains private user information at the privacy server 108 while using applications or services hosted on other servers. For example, the privacy server 108 may be used to maintain private data of a user within one jurisdiction even though the user is accessing an application hosted on a server (e.g., the content management server 102) located outside the jurisdiction. In such cases, the privacy server 108 may intercept communications between a user device 106 or supervisor device 110 and other devices that include private user information. The privacy server 108 may create a token or identifier that does not disclose the private information and may use the token or identifier when communicating with the other servers and systems, instead of using the user's private information.

As illustrated in FIG. 1, the content management server 102 may be in communication with one or more additional servers, such as a content server 112, a user data server 112, and/or an administrator server 116. Each of these servers may include some or all of the same physical and logical components as the content management server(s) 102, and in some cases, the hardware and software components of these servers 112-116 may be incorporated into the content management server(s) 102, rather than being implemented as separate computer servers.

Content server 112 may include hardware and software components to generate, store, and maintain the content resources for distribution to user devices 106 and other devices in the network 100. For example, in content distribution networks 100 used for professional training and educational purposes, content server 112 may include data stores of training materials, presentations, plans, syllabi, reviews, evaluations, interactive programs and simulations, course models, course outlines, and various training interfaces that correspond to different materials and/or different types of user devices 106. In content distribution networks 100 used for media distribution, interactive gaming, and the like, a content server 112 may include media content files such as music, movies, television programming, games, and advertisements.

User data server 114 may include hardware and software components that store and process data for multiple users relating to each user's activities and usage of the content distribution network 100. For example, the content management server 102 may record and track each user's system usage, including his or her user device 106, content resources accessed, and interactions with other user devices 106. This data may be stored and processed by the user data server 114, to support user tracking and analysis features. For instance, in the professional training and educational contexts, the user data server 114 may store and analyze each user's training materials viewed, presentations attended, courses completed, interactions, evaluation results, and the like. The user data server 114 may also include a repository for user-generated material, such as evaluations and tests completed by users, and documents and assignments prepared by users. In the context of media distribution and interactive gaming, the user data server 114 may store and process resource access data for multiple users (e.g., content titles accessed, access times, data usage amounts, gaming histories, user devices and device types, etc.).

Administrator server 116 may include hardware and software components to initiate various administrative functions at the content management server 102 and other components within the content distribution network 100. For example, the administrator server 116 may monitor device status and performance for the various servers, data stores, and/or user devices 106 in the content distribution network 100. When necessary, the administrator server 116 may add or remove devices from the network 100, and perform device maintenance such as providing software updates to the devices in the network 100. Various administrative tools on the administrator server 116 may allow authorized users to set user access permissions to various content resources, monitor resource usage by users and devices 106, and perform analyses and generate reports on specific network users and/or devices (e.g., resource usage tracking reports, training evaluations, etc.).

The content distribution network 100 may include one or more communication networks 120. Although only a single network 120 is identified in FIG. 1, the content distribution network 100 may include any number of different communication networks between any of the computer servers and devices shown in FIG. 1 and/or other devices described herein. Communication networks 120 may enable communication between the various computing devices, servers, and other components of the content distribution network 100. As discussed below, various implementations of content distribution networks 100 may employ different types of networks 120, for example, computer networks, telecommunications networks, wireless networks, and/or any combination of these and/or other networks.

The content distribution network 100 may include one or several navigation systems or features including, for example, the Global Positioning System (“GPS”), GALILEO, or the like, or location systems or features including, for example, one or several transceivers that can determine location of the one or several components of the content distribution network 100 via, for example, triangulation. All of these are depicted as navigation system 122.

In some embodiments, navigation system 122 can include one or several features that can communicate with one or several components of the content distribution network 100 including, for example, with one or several of the user devices 106 and/or with one or several of the supervisor devices 110. In some embodiments, this communication can include the transmission of a signal from the navigation system 122 which signal is received by one or several components of the content distribution network 100 and can be used to determine the location of the one or several components of the content distribution network 100.

With reference to FIG. 2, an illustrative distributed computing environment 200 is shown including a computer server 202, four client computing devices 206, and other components that may implement certain embodiments and features described herein. In some embodiments, the server 202 may correspond to the content management server 102 discussed above in FIG. 1, and the client computing devices 206 may correspond to the user devices 106. However, the computing environment 200 illustrated in FIG. 2 may correspond to any other combination of devices and servers configured to implement a client-server model or other distributed computing architecture.

Client devices 206 may be configured to receive and execute client applications over one or more networks 220. Such client applications may be web browser-based applications and/or standalone software applications, such as mobile device applications. Server 202 may be communicatively coupled with the client devices 206 via one or more communication networks 220. Client devices 206 may receive client applications from server 202 or from other application providers (e.g., public or private application stores). Server 202 may be configured to run one or more server software applications or services, for example, web-based or cloud-based services, to support content distribution and interaction with client devices 206. Users operating client devices 206 may in turn utilize one or more client applications (e.g., virtual client applications) to interact with server 202 to utilize the services provided by these components.

Various different subsystems and/or components 204 may be implemented on server 202. Users operating the client devices 206 may initiate one or more client applications to use services provided by these subsystems and components. The subsystems and components within the server 202 and client devices 206 may be implemented in hardware, firmware, software, or combinations thereof. Various different system configurations are possible in different distributed computing systems 200 and content distribution networks 100. The embodiment shown in FIG. 2 is thus one example of a distributed computing system and is not intended to be limiting.

Although exemplary computing environment 200 is shown with four client computing devices 206, any number of client computing devices may be supported. Other devices, such as specialized sensor devices, etc., may interact with client devices 206 and/or server 202.

As shown in FIG. 2, various security and integration components 208 may be used to send and manage communications between the server 202 and user devices 206 over one or more communication networks 220. The security and integration components 208 may include separate servers, such as web servers and/or authentication servers, and/or specialized networking components, such as firewalls, routers, gateways, load balancers, and the like. In some cases, the security and integration components 208 may correspond to a set of dedicated hardware and/or software operating at the same physical location and under the control of same entities as server 202. For example, components 208 may include one or more dedicated web servers and network hardware in a datacenter or a cloud infrastructure. In other examples, the security and integration components 208 may correspond to separate hardware and software components which may be operated at a separate physical location and/or by a separate entity.

Security and integration components 208 may implement various security features for data transmission and storage, such as authenticating users and restricting access to unknown or unauthorized users. In various implementations, security and integration components 208 may provide, for example, a file-based integration scheme or a service-based integration scheme for transmitting data between the various devices in the content distribution network 100. Security and integration components 208 also may use secure data transmission protocols and/or encryption for data transfers, for example, File Transfer Protocol (FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy (PGP) encryption.

In some embodiments, one or more web services may be implemented within the security and integration components 208 and/or elsewhere within the content distribution network 100. Such web services, including cross-domain and/or cross-platform web services, may be developed for enterprise use in accordance with various web service standards, such as RESTful web services (i.e., services based on the Representation State Transfer (REST) architectural style and constraints), and/or web services designed in accordance with the Web Service Interoperability (WS-I) guidelines. Some web services may use the Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocol to provide secure connections between the server 202 and user devices 206. SSL or TLS may use HTTP or HTTPS to provide authentication and confidentiality. In other examples, web services may be implemented using REST over HTTPS with the OAuth open standard for authentication, or using the WS-Security standard which provides for secure SOAP messages using XML encryption. In other examples, the security and integration components 208 may include specialized hardware for providing secure web services. For example, security and integration components 208 may include secure network appliances having built-in features such as hardware-accelerated SSL and HTTPS, WS-Security, and firewalls. Such specialized hardware may be installed and configured in front of any web servers, so that any external devices may communicate directly with the specialized hardware.

Communication network(s) 220 may be any type of network familiar to those skilled in the art that can support data communications using any of a variety of commercially-available protocols, including without limitation, TCP/IP (transmission control protocol/Internet protocol), SNA (systems network architecture), IPX (Internet packet exchange), Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols, Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text Transfer Protocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and the like. Merely by way of example, network(s) 220 may be local area networks (LAN), such as one based on Ethernet, Token-Ring and/or the like. Network(s) 220 also may be wide-area networks, such as the Internet. Networks 220 may include telecommunication networks such as public switched telephone networks (PSTNs), or virtual networks such as an intranet or an extranet. Infrared and wireless networks (e.g., using the Institute of Electrical and Electronics (IEEE) 802.11 protocol suite or other wireless protocols) also may be included in networks 220.

Computing environment 200 also may include one or more data stores 210 and/or back-end servers 212. In certain examples, the data stores 210 may correspond to data store server(s) 104 discussed above in FIG. 1, and back-end servers 212 may correspond to the various back-end servers 112-116. Data stores 210 and servers 212 may reside in the same datacenter or may operate at a remote location from server 202. In some cases, one or more data stores 210 may reside on a non-transitory storage medium within the server 202. Other data stores 210 and back-end servers 212 may be remote from server 202 and configured to communicate with server 202 via one or more networks 220. In certain embodiments, data stores 210 and back-end servers 212 may reside in a storage-area network (SAN), or may use storage-as-a-service (STaaS) architectural model.

With reference to FIG. 3, an illustrative set of data stores and/or data store servers is shown, corresponding to the data store servers 104 of the content distribution network 100 discussed above in FIG. 1. One or more individual data stores 301-311 may reside in storage on a single computer server 104 (or a single server farm or cluster) under the control of a single entity, or may reside on separate servers operated by different entities and/or at remote locations. In some embodiments, data stores 301-311 may be accessed by the content management server 102 and/or other devices and servers within the network 100 (e.g., user devices 106, supervisor devices 110, administrator servers 116, etc.). Access to one or more of the data stores 301-311 may be limited or denied based on the processes, user credentials, and/or devices attempting to interact with the data store.

The paragraphs below describe examples of specific data stores that may be implemented within some embodiments of a content distribution network 100. It should be understood that the below descriptions of data stores 301-311, including their functionality and types of data stored therein, are illustrative and non-limiting. Data stores server architecture, design, and the execution of specific data stores 301-311 may depend on the context, size, and functional requirements of a content distribution network 100. For example, in content distribution systems 100 used for professional training and educational purposes, separate databases or file-based storage systems may be implemented in data store server(s) 104 to store trainee and/or student data, trainer and/or professor data, training module data and content descriptions, training results, evaluation data, and the like. In contrast, in content distribution systems 100 used for media distribution from content providers to subscribers, separate data stores may be implemented in data stores server(s) 104 to store listings of available content titles and descriptions, content title usage statistics, subscriber profiles, account data, payment data, network usage statistics, etc.

A user profile data store 301, also referred to herein as a user profile database 301, may include information relating to the end users within the content distribution network 100. This information may include user characteristics such as the user names, access credentials (e.g., logins and passwords), user preferences, and information relating to any previous user interactions within the content distribution network 100 (e.g., requested content, posted content, content modules completed, training scores or evaluations, other associated users, etc.). In some embodiments, this information can relate to one or several individual end users such as, for example, one or several students, teachers, administrators, or the like, and in some embodiments, this information can relate to one or several institutional end users such as, for example, one or several schools, groups of schools such as one or several school districts, one or several colleges, one or several universities, one or several training providers, or the like. In some embodiments, this information can identify one or several user memberships in one or several groups such as, for example, a student's membership in a university, school, program, grade, course, class, or the like.

The user profile database 301 can include information relating to a user's status, location, or the like. This information can identify, for example, a device a user is using, the location of that device, or the like. In some embodiments, this information can be generated based on any location detection technology including, for example, a navigation system 122, or the like.

Information relating to the user's status can identify, for example, logged-in status information that can indicate whether the user is presently logged-in to the content distribution network 100 and/or whether the log-in-is active. In some embodiments, the information relating to the user's status can identify whether the user is currently accessing content and/or participating in an activity from the content distribution network 100.

In some embodiments, information relating to the user's status can identify, for example, one or several attributes of the user's interaction with the content distribution network 100, and/or content distributed by the content distribution network 100. This can include data identifying the user's interactions with the content distribution network 100, the content consumed by the user through the content distribution network 100, or the like. In some embodiments, this can include data identifying the type of information accessed through the content distribution network 100 and/or the type of activity performed by the user via the content distribution network 100, the lapsed time since the last time the user accessed content and/or participated in an activity from the content distribution network 100, or the like. In some embodiments, this information can relate to a content program comprising an aggregate of data, content, and/or activities, and can identify, for example, progress through the content program, or through the aggregate of data, content, and/or activities forming the content program. In some embodiments, this information can track, for example, the amount of time since participation in and/or completion of one or several types of activities, the amount of time since communication with one or several supervisors and/or supervisor devices 110, or the like.

In some embodiments the user profile database 30 may include personalized automatically generated aggregated content for one or more subject for one or more users. This is described more fully below for FIGS. 19-21. User's may select content files from which personalized aggregated content may be created. Once created, the personalized aggregated content may be presented to the user to facilitate mastering the subject matter of the personalized aggregated content.

In some embodiments the user profile database 30 may include extracted words or noun phrases that may have been automatically extracted from the content provided by the user. Such words or noun phrases may form the bases for presenting multi-choice answers for incomplete clauses as further described in FIGS. 19-21.

In some embodiments in which the one or several end users are individuals, and specifically are students, the user profile database 301 can further include information relating to these students' academic and/or educational history. This information can identify one or several courses of study that the student has initiated, completed, and/or partially completed, as well as grades received in those courses of study. In some embodiments, the student's academic and/or educational history can further include information identifying student performance on one or several tests, quizzes, and/or assignments. In some embodiments, this information can be stored in a tier of memory that is not the fastest memory in the content delivery network 100.

The user profile database 301 can include information relating to one or several student learning preferences. In some embodiments, for example, the user, also referred to herein as the student or the student-user may have one or several preferred learning styles, one or several most effective learning styles, and/or the like. In some embodiments, the student's learning style can be any learning style describing how the student best learns or how the student prefers to learn. In one embodiment, these learning styles can include, for example, identification of the student as an auditory learner, as a visual learner, and/or as a tactile learner. In some embodiments, the data identifying one or several student learning styles can include data identifying a learning style based on the student's educational history such as, for example, identifying a student as an auditory learner when the student has received significantly higher grades and/or scores on assignments and/or in courses favorable to auditory learners. In some embodiments, this information can be stored in a tier of memory that is not the fastest memory in the content delivery network 100.

In some embodiments, the user profile data store 301 can further include information identifying one or several user skill levels. In some embodiments, these one or several user skill levels can identify a skill level determined based on past performance by the user interacting with the content delivery network 100, and in some embodiments, these one or several user skill levels can identify a predicted skill level determined based on past performance by the user interacting with the content delivery network 100 and one or several predictive models.

The user profile database 301 can further include information relating to one or several teachers and/or instructors who are responsible for organizing, presenting, and/or managing the presentation of information to the student. In some embodiments, user profile database 301 can include information identifying courses and/or subjects that have been taught by the teacher, data identifying courses and/or subjects currently taught by the teacher, and/or data identifying courses and/or subjects that will be taught by the teacher. In some embodiments, this can include information relating to one or several teaching styles of one or several teachers. In some embodiments, the user profile database 301 can further include information indicating past evaluations and/or evaluation reports received by the teacher. In some embodiments, the user profile database 301 can further include information relating to improvement suggestions received by the teacher, training received by the teacher, continuing education received by the teacher, and/or the like. In some embodiments, this information can be stored in a tier of memory that is not the fastest memory in the content delivery network 100.

An accounts data store 302, also referred to herein as an accounts database 302, may generate and store account data for different users in various roles within the content distribution network 100. For example, accounts may be created in an accounts data store 302 for individual end users, supervisors, administrator users, and entities such as companies or educational institutions. Account data may include account types, current account status, account characteristics, and any parameters, limits, restrictions associated with the accounts.

A content library data store 303, also referred to herein as a content library database 303, may include information describing the individual content items (or content resources or data packets) available via the content distribution network 100. In some embodiments, these data packets in the content library database 303 can be linked to form an object network. In some embodiments, these data packets can be linked in the object network according to one or several prerequisite relationships that can, for example, identify the relative hierarchy and/or difficulty of the data objects. In some embodiments, this hierarchy of data objects can be generated by the content distribution network 100 according to user experience with the object network, and in some embodiments, this hierarchy of data objects can be generated based on one or several existing and/or external hierarchies such as, for example, a syllabus, a table of contents, or the like. In some embodiments, for example, the object network can correspond to a syllabus such that content for the syllabus is embodied in the object network.

In some embodiments, the content library data store 303 can comprise a syllabus, a schedule, or the like. In some embodiments, the syllabus or schedule can identify one or several tasks and/or events relevant to the user. In some embodiments, for example, when the user is a member of a group such as a section or a class, these tasks and/or events relevant to the user can identify one or several assignments, quizzes, exams, or the like.

In some embodiment, the content library data store 303 may contain content that may be used to automatically generate personalized aggregated content for a user as described more fully below for FIGS. 19-21. This content may include a syllabus, tasks, events, assignments, quizzes, exams or the like. This content may include a digital textbook assigned for the course.

In some embodiments, the library data store 303 may include metadata, properties, and other characteristics associated with the content resources stored in the content server 112. Such data may identify one or more aspects or content attributes of the associated content resources, for example, subject matter, access level, or skill level of the content resources, license attributes of the content resources (e.g., any limitations and/or restrictions on the licensable use and/or distribution of the content resource), price attributes of the content resources (e.g., a price and/or price structure for determining a payment amount for use or distribution of the content resource), rating attributes for the content resources (e.g., data indicating the evaluation or effectiveness of the content resource), and the like. In some embodiments, the library data store 303 may be configured to allow updating of content metadata or properties, and to allow the addition and/or removal of information relating to the content resources. For example, content relationships may be implemented as graph structures, which may be stored in the library data store 303 or in an additional store for use by selection algorithms along with the other metadata.

In some embodiments, the content library data store 303 can contain information used in evaluating responses received from users. In some embodiments, for example, a user can receive content from the content distribution network 100 and can, subsequent to receiving that content, provide a response to the received content. In some embodiments, for example, the received content can comprise one or several questions, prompts, flashcards, or the like, and the response to the received content can comprise an answer to those one or several questions, prompts, or the like. In some embodiments, information, referred to herein as “comparative data,” from the content library data store 303 can be used to determine whether the responses are the correct and/or desired responses.

In some embodiments, the content library database 303 and/or the user profile database 301 can comprise an aggregation network, also referred to herein as a content network or content aggregation network. The aggregation network can comprise a plurality of content aggregations that can be linked together by, for example: creation by common user; relation to a common subject, topic, skill, or the like; creation from a common set of source material such as source data packets; or the like. In some embodiments, the content aggregation can comprise a grouping of content comprising the presentation portion that can be provided to the user in the form of, for example, a flashcard and an extraction portion that can comprise the desired response to the presentation portion such as for example, an answer to a flashcard. In some embodiments, one or several content aggregations can be generated by the content distribution network 100 and can be related to one or several data packets that can be, for example, organized in object network. In some embodiments, the one or several content aggregations can be each created from content stored in one or several of the data packets.

In some embodiments, the content aggregations located in the content library database 303 and/or the user profile database 301 can be associated with a user-creator of those content aggregations. In some embodiments, access to content aggregations can vary based on, for example, whether a user created the content aggregations. In some embodiments, the content library database 303 and/or the user profile database 301 can comprise a database of content aggregations associated with a specific user, and in some embodiments, the content library database 303 and/or the user profile database 301 can comprise a plurality of databases of content aggregations that are each associated with a specific user. In some embodiments, these databases of content aggregations can include content aggregations created by their specific user and, in some embodiments, these databases of content aggregations can further include content aggregations selected for inclusion by their specific user and/or a supervisor of that specific user. In some embodiments, these content aggregations can be arranged and/or linked in a hierarchical relationship similar to the data packets in the object network and/or linked to the object network in the object network or the tasks or skills associated with the data packets in the object network or the syllabus or schedule.

In some embodiments, the content aggregation network, and the content aggregations forming the content aggregation network can be organized according to the object network and/or the hierarchical relationships embodied in the object network. In some embodiments, the content aggregation network, and/or the content aggregations forming the content aggregation network can be organized according to one or several tasks identified in the syllabus, schedule or the like.

A pricing data store 304 may include pricing information and/or pricing structures for determining payment amounts for providing access to the content distribution network 100 and/or the individual content resources within the network 100. In some cases, pricing may be determined based on a user's access to the content distribution network 100, for example, a time-based subscription fee, or pricing based on network usage. In other cases, pricing may be tied to specific content resources. Certain content resources may have associated pricing information, whereas other pricing determinations may be based on the resources accessed, the profiles and/or accounts of the user, and the desired level of access (e.g., duration of access, network speed, etc.). Additionally, the pricing data store 304 may include information relating to compilation pricing for groups of content resources, such as group prices and/or price structures for groupings of resources.

A license data store 305 may include information relating to licenses and/or licensing of the content resources within the content distribution network 100. For example, the license data store 305 may identify licenses and licensing terms for individual content resources and/or compilations of content resources in the content server 112, the rights holders for the content resources, and/or common or large-scale right holder information such as contact information for rights holders of content not included in the content server 112.

A content access data store 306 may include access rights and security information for the content distribution network 100 and specific content resources. For example, the content access data store 306 may include login information (e.g., user identifiers, logins, passwords, etc.) that can be verified during user login attempts to the network 100. The content access data store 306 also may be used to store assigned user roles and/or user levels of access. For example, a user's access level may correspond to the sets of content resources and/or the client or server applications that the user is permitted to access. Certain users may be permitted or denied access to certain applications and resources based on their subscription level, training program, course/grade level, etc. Certain users may have supervisory access over one or more end users, allowing the supervisor to access all or portions of the end user's content, activities, evaluations, etc. Additionally, certain users may have administrative access over some users and/or some applications in the content management network 100, allowing such users to add and remove user accounts, modify user access permissions, perform maintenance updates on software and servers, etc.

A source data store 307 may include information relating to the source of the content resources available via the content distribution network. For example, a source data store 307 may identify the authors and originating devices of content resources, previous pieces of data and/or groups of data originating from the same authors or originating devices, and the like.

An evaluation data store 308 may include information used to direct the evaluation of users and content resources in the content management network 100. In some embodiments, the evaluation data store 308 may contain, for example, the analysis criteria and the analysis guidelines for evaluating users (e.g., trainees/students, gaming users, media content consumers, etc.) and/or for evaluating the content resources in the network 100. The evaluation data store 308 also may include information relating to evaluation processing tasks, for example, the identification of users and user devices 106 that have received certain content resources or accessed certain applications, the status of evaluations or evaluation histories for content resources, users, or applications, and the like. Evaluation criteria may be stored in the evaluation data store 308 including data and/or instructions in the form of one or several electronic rubrics or scoring guides for use in the evaluation of the content, users, or applications. The evaluation data store 308 also may include past evaluations and/or evaluation analyses for users, content, and applications, including relative rankings, characterizations, explanations, and the like.

The evaluation data store 308 may include evaluation analysis that may be used to determine the order and frequency of personalized aggregated content presentation to a user as discussed more fully below in FIGS. 20-21. Evaluation analysis can determine if a user needs more frequent, less frequent, and/or different ordering of the personalized aggregated content presentation. For instance—if the evaluation analysis determines the student takes more time for a presentation and subsequent response to an incomplete clause—the frequency of the presentation may be changed based on the number of incomplete clauses the user needs to master and the amount of time before mastery is required. As a further example—the evaluation analysis may indicate that a student has mastered the least difficult subject matter and so does not need more presentation of that matter.

A model data store 309, also referred to herein as a model database 309, can store information relating to one or several predictive models. In some embodiments, these can include one or several evidence models, risk models, skill models, or the like. In some embodiments, an evidence model can be a mathematically-based statistical model. The evidence model can be based on, for example, Item Response Theory (IRT), Bayesian Network (Bayes net), Performance Factor Analysis (PFA), or the like. The evidence model can, in some embodiments, be customizable to a user and/or to one or several content items. Specifically, one or several inputs relating to the user and/or to one or several content items can be inserted into the evidence model. These inputs can include, for example, one or several measures of user skill level, one or several measures of content item difficulty and/or skill level, or the like. The customized evidence model can then be used to predict the likelihood of the user providing desired or undesired responses to one or several of the content items.

In some embodiments, the risk models can include one or several models that can be used to calculate one or several model function values. In some embodiments, these one or several model function values can be used to calculate a risk probability, which risk probability can characterize the risk of a user such as a student-user failing to achieve a desired outcome such as, for example, failing to correctly respond to one or several data packets, failure to achieve a desired level of completion of a program, for example in a pre-defined time period, failure to achieve a desired learning outcome, or the like. In some embodiments, the risk probability can identify the risk of the student-user failing to complete 60% of the program.

In some embodiments, these models can include a plurality of model functions including, for example, a first model function, a second model function, a third model function, and a fourth model function. In some embodiments, some or all of the model functions can be associated with a portion of the program such as, for example, a completion stage and/or completion status of the program. In one embodiment, for example, the first model function can be associated with a first completion status, the second model function can be associated with a second completion status, the third model function can be associated with a third completion status, and the fourth model function can be associated with a fourth completion status. In some embodiments, these completion statuses can be selected such that some or all of these completion statuses are less than the desired level of completion of the program. Specifically, in some embodiments, these completion statuses can be selected to all be at less than 60% completion of the program, and more specifically, in some embodiments, the first completion status can be at 20% completion of the program, the second completion status can be at 30% completion of the program, the third completion status can be at 40% completion of the program, and the fourth completion status can be at 50% completion of the program. Similarly, any desired number of model functions can be associated with any desired number of completion statuses.

In some embodiments, a model function can be selected from the plurality of model functions based on a student-user's progress through a program. In some embodiments, the student-user's progress can be compared to one or several status trigger thresholds, each of which status trigger thresholds can be associated with one or more of the model functions. If one of the status triggers is triggered by the student-user's progress, the corresponding one or several model functions can be selected.

The model functions can comprise a variety of types of models and/or functions. In some embodiments, each of the model functions outputs a function value that can be used in calculating a risk probability. This function value can be calculated by performing one or several mathematical operations on one or several values indicative of one or several user attributes and/or user parameters, also referred to herein as program status parameters. In some embodiments, each of the model functions can use the same program status parameters, and in some embodiments, the model functions can use different program status parameters. In some embodiments, the model functions use different program status parameters when at least one of the model functions uses at least one program status parameter that is not used by others of the model functions.

In some embodiments, a skill model can comprise a statistical model identifying a predictive skill level of one or several students. In some embodiments, this model can identify a single skill level of a student and/or a range of possible skill levels of a student. In some embodiments, this statistical model can identify a skill level of a student-user and an error value or error range associated with that skill level. In some embodiments, the error value can be associated with a confidence interval determined based on a confidence level. Thus, in some embodiments, as the number of student interactions with the content distribution network increases, the confidence level can increase and the error value can decrease such that the range identified by the error value about the predicted skill level is smaller.

In some embodiments, the model data store 309 may comprise a model for personalized content aggregation presentation. For instance, the model may determine from the number of clauses and the amount of time be for mastery is required, the order and frequency of presentation of the incomplete clauses.

A threshold database 310, also referred to herein as a threshold database, can store one or several threshold values. These one or several threshold values can delineate between states or conditions. In one exemplary embodiment, for example, a threshold value can delineate between an acceptable user performance and an unacceptable user performance, between content appropriate for a user and content that is inappropriate for a user, between risk levels, or the like.

In addition to the illustrative data stores described above, data store server(s) 104 (e.g., database servers, file-based storage servers, etc.) may include one or more external data aggregators 311. External data aggregators 311 may include third-party data sources accessible to the content management network 100, but not maintained by the content management network 100. External data aggregators 311 may include any electronic information source relating to the users, content resources, or applications of the content distribution network 100. For example, external data aggregators 311 may be third-party data stores containing demographic data, education-related data, consumer sales data, health-related data, and the like. Illustrative external data aggregators 311 may include, for example, social networking web servers, public records data stores, learning management systems, educational institution servers, business servers, consumer sales data stores, medical record data stores, etc. Data retrieved from various external data aggregators 311 may be used to verify and update user account information, suggest user content, and perform user and content evaluations.

With reference now to FIG. 4, a block diagram is shown illustrating an embodiment of one or more content management servers 102 within a content distribution network 100. In such an embodiment, content management server 102 performs internal data gathering and processing of streamed content along with external data gathering and processing. Other embodiments could have either all external or all internal data gathering. This embodiment allows reporting timely information that might be of interest to the reporting party or other parties. In this embodiment, the content management server 102 can monitor gathered information from several sources to allow it to make timely business and/or processing decisions based upon that information. For example, reports of user actions and/or responses, as well as the status and/or results of one or several processing tasks could be gathered and reported to the content management server 102 from a number of sources.

Internally, the content management server 102 gathers information from one or more internal components 402-408. The internal components 402-408 gather and/or process information relating to such things as: content provided to users; content consumed by users; responses provided by users; user skill levels; content difficulty levels; next content for providing to users; etc. The internal components 402-408 can report the gathered and/or generated information in real-time, near real-time or along another time line. To account for any delay in reporting information, a time stamp or staleness indicator can inform others of how timely the information was sampled. The content management server 102 can opt to allow third parties to use internally or externally gathered information that is aggregated within the server 102 by subscription to the content distribution network 100.

A command and control (CC) interface 338 configures the gathered input information to an output of data streams, also referred to herein as content streams. APIs for accepting gathered information and providing data streams are provided to third parties external to the server 102 who want to subscribe to data streams. The server 102 or a third party can design as yet undefined APIs using the CC interface 338. The server 102 can also define authorization and authentication parameters using the CC interface 338 such as authentication, authorization, login, and/or data encryption. CC information is passed to the internal components 402-408 and/or other components of the content distribution network 100 through a channel separate from the gathered information or data stream in this embodiment, but other embodiments could embed CC information in these communication channels. The CC information allows throttling information reporting frequency, specifying formats for information and data streams, deactivation of one or several internal components 402-408 and/or other components of the content distribution network 100, updating authentication and authorization, etc.

The various data streams that are available can be researched and explored through the CC interface 338. Those data stream selections for a particular subscriber, which can be one or several of the internal components 402-408 and/or other components of the content distribution network 100, are stored in the queue subscription information database 322. The server 102 and/or the CC interface 338 then routes selected data streams to processing subscribers that have selected delivery of a given data stream. Additionally, the server 102 also supports historical queries of the various data streams that are stored in an historical data store 334 as gathered by an archive data agent 336. Through the CC interface 238 various data streams can be selected for archiving into the historical data store 334.

Components of the content distribution network 100 outside of the server 102 can also gather information that is reported to the server 102 in real-time, near real-time or along another time line. There is a defined API between those components and the server 102. Each type of information or variable collected by server 102 falls within a defined API or multiple APIs. In some cases, the CC interface 338 is used to define additional variables to modify an API that might be of use to processing subscribers. The additional variables can be passed to all processing subscribes or just a subset. For example, a component of the content distribution network 100 outside of the server 102 may report a user response but define an identifier of that user as a private variable that would not be passed to processing subscribers lacking access to that user and/or authorization to receive that user data. Processing subscribers having access to that user and/or authorization to receive that user data would receive the subscriber identifier along with response reported that component. Encryption and/or unique addressing of data streams or sub-streams can be used to hide the private variables within the messaging queues.

The user devices 106 and/or supervisor devices 110 communicate with the server 102 through security and/or integration hardware 410. The communication with security and/or integration hardware 410 can be encrypted or not. For example, a socket using a TCP connection could be used. In addition to TCP, other transport layer protocols like SCTP and UDP could be used in some embodiments to intake the gathered information. A protocol such as SSL could be used to protect the information over the TCP connection. Authentication and authorization can be performed to any user devices 106 and/or supervisor device interfacing to the server 102. The security and/or integration hardware 410 receives the information from one or several of the user devices 106 and/or the supervisor devices 110 by providing the API and any encryption, authorization, and/or authentication. In some cases, the security and/or integration hardware 410 reformats or rearranges this received information.

The messaging bus 412, also referred to herein as a messaging queue or a messaging channel, can receive information from the internal components of the server 102 and/or components of the content distribution network 100 outside of the server 102 and distribute the gathered information as a data stream to any processing subscribers that have requested the data stream from the messaging queue 412. Specifically, in some embodiments, the messaging bus 412 can receive and output information from at least one of the packet selection system 402, the presentation system 408, the response system 406, and the summary model system 404. In some embodiments, this information can be output according to a “push” model, and in some embodiments, this information can be output according to a “pull” model.

As indicated in FIG. 4, processing subscribers are indicated by a connector to the messaging bus 412, the connector having an arrow head pointing away from the messaging bus 412. Only data streams within the messaging queue 412 that a particular processing subscriber has subscribed to may be read by that processing subscriber if received at all. Gathered information sent to the messaging queue 412 is processed and returned in a data stream in a fraction of a second by the messaging queue 412. Various multicasting and routing techniques can be used to distribute a data stream from the messaging queue 412 that a number of processing subscribers have requested. Protocols such as Multicast or multiple Unicast could be used to distribute streams within the messaging queue 412. Additionally, transport layer protocols like TCP, SCTP and UDP could be used in various embodiments.

Through the CC interface 338, an external or internal processing subscriber can be assigned one or more data streams within the messaging queue 412. A data stream is a particular type of message in a particular category. For example, a data stream can comprise all of the data reported to the messaging bus 412 by a designated set of components. One or more processing subscribers could subscribe and receive the data stream to process the information and make a decision and/or feed the output from the processing as gathered information fed back into the messaging queue 412. Through the CC interface 338 a developer can search the available data streams or specify a new data stream and its API. The new data stream might be determined by processing a number of existing data streams with a processing subscriber.

The CDN 110 has internal processing subscribers 402-408 that process assigned data streams to perform functions within the server 102. Internal processing subscribers 402-408 could perform functions such as providing content to a user, receiving a response from a user, determining the correctness of the received response, updating one or several models based on the correctness of the response, recommending new content for providing to one or several users, or the like. The internal processing subscribers 402-408 can decide filtering and weighting of records from the data stream. To the extent that decisions are made based upon analysis of the data stream, each data record is time stamped to reflect when the information was gathered such that additional credibility could be given to more recent results, for example. Other embodiments may filter out records in the data stream that are from an unreliable source or stale. For example, a particular contributor of information may prove to have less than optimal gathered information and that could be weighted very low or removed altogether.

Internal processing subscribers 402-408 may additionally process one or more data streams to provide different information to feed back into the messaging queue 412 to be part of a different data stream. For example, hundreds of user devices 106 could provide responses that are put into a data stream on the messaging queue 412. An internal processing subscriber 402-408 could receive the data stream and process it to determine the difficulty of one or several data packets provided to one or several users, and supply this information back onto the messaging queue 412 for possible use by other internal and external processing subscribers.

As mentioned above, the CC interface 338 allows the CDN 110 to query historical messaging queue 412 information. An archive data agent 336 listens to the messaging queue 412 to store data streams in a historical database 334. The historical database 334 may store data streams for varying amounts of time and may not store all data streams. Different data streams may be stored for different amounts of time.

With regard to the components 402-408, the content management server(s) 102 may include various server hardware and software components that manage the content resources within the content distribution network 100 and provide interactive and adaptive content to users on various user devices 106. For example, content management server(s) 102 may provide instructions to and receive information from the other devices within the content distribution network 100, in order to manage and transmit content resources, user data, and server or client applications executing within the network 100.

A content management server 102 may include a packet selection system 402. The packet selection system 402 may be implemented using dedicated hardware within the content distribution network 100 (e.g., a packet selection server 402), or using designated hardware and software resources within a shared content management server 102. In some embodiments, the packet selection system 402 may adjust the selection and adaptive capabilities of content resources to match the needs and desires of the users receiving the content. For example, the packet selection system 402 may query various data stores and servers 104 to retrieve user information, such as user preferences and characteristics (e.g., from a user profile data store 301), user access restrictions to content recourses (e.g., from a content access data store 306), previous user results and content evaluations (e.g., from an evaluation data store 308), and the like. Based on the retrieved information from data stores 104 and other data sources, the packet selection system 402 may modify content resources for individual users.

In some embodiments, the packet selection system 402 can include a recommendation engine, also referred to herein as an adaptive recommendation engine. In some embodiments, the recommendation engine can select one or several pieces of content, also referred to herein as data packets, for providing to a user. These data packets can be selected based on, for example, the information retrieved from the database server 104 including, for example, the user profile database 301, the content library database 303, the model database 309, or the like. In some embodiments, these one or several data packets can be adaptively selected and/or selected according to one or several selection rules. In one embodiment, for example, the recommendation engine can retrieve information from the user profile database 301 identifying, for example, a skill level of the user. The recommendation engine can further retrieve information from the content library database 303 identifying, for example, potential data packets for providing to the user and the difficulty of those data packets and/or the skill level associated with those data packets.

The recommendation engine can identify one or several potential data packets for providing and/or one or several data packets for providing to the user based on, for example, one or several rules, models, predictions, or the like. The recommendation engine can use the skill level of the user to generate a prediction of the likelihood of one or several users providing a desired response to some or all of the potential data packets. In some embodiments, the recommendation engine can pair one or several data packets with selection criteria that may be used to determine which packet should be delivered to a student-user based on one or several received responses from that student-user. In some embodiments, one or several data packets can be eliminated from the pool of potential data packets if the prediction indicates either too high a likelihood of a desired response or too low a likelihood of a desired response. In some embodiments, the recommendation engine can then apply one or several selection criteria to the remaining potential data packets to select a data packet for providing to the user. These one or several selection criteria can be based on, for example, criteria relating to a desired estimated time for receipt of response to the data packet, one or several content parameters, one or several assignment parameters, or the like.

In some embodiments the packet selection system 402 may select the data packets or incomplete clauses from user provided content when automatically generating personalized aggregated content. In this embodiment the packet selection system 402 may parse four words into an independent clause containing a verb and a noun. The packet selection system may further extract a noun or noun phase (hereinafter used interchangeably as a noun is also a noun phrase) from the independent clause to form an incomplete clause that is the basis for a fill-in-the blank content presentation.

A content management server 102 also may include a summary model system 404. The summary model system 404 may be implemented using dedicated hardware within the content distribution network 100 (e.g., a summary model server 404), or using designated hardware and software resources within a shared content management server 102. In some embodiments, the summary model system 404 may monitor the progress of users through various types of content resources and groups, such as media compilations, courses or curriculums in training or educational contexts, interactive gaming environments, and the like. For example, the summary model system 404 may query one or more databases and/or data store servers 104 to retrieve user data such as associated content compilations or programs, content completion status, user goals, results, and the like.

In some embodiments, the summary model system 404 may also comprise models for the order and frequency of content aggregation to a user. The summary model system may adapt these models based on multiple user results over time.

A content management server 102 also may include a response system 406, which can include, in some embodiments, a response processor. The response system 406 may be implemented using dedicated hardware within the content distribution network 100 (e.g., a response server 406), or using designated hardware and software resources within a shared content management server 102. The response system 406 may be configured to receive and analyze information from user devices 106. For example, various ratings of content resources submitted by users may be compiled and analyzed, and then stored in a data store (e.g., a content library data store 303 and/or evaluation data store 308) associated with the content. In some embodiments, the response server 406 may analyze the information to determine the effectiveness or appropriateness of content resources with, for example, a subject matter, an age group, a skill level, or the like. In some embodiments, the response system 406 may provide updates to the packet selection system 402 or the summary model system 404, with the attributes of one or more content resources or groups of resources within the network 100. The response system 406 also may receive and analyze user evaluation data from user devices 106, supervisor devices 110, and administrator servers 116, etc. For instance, response system 406 may receive, aggregate, and analyze user evaluation data for different types of users (e.g., end users, supervisors, administrators, etc.) in different contexts (e.g., media consumer ratings, trainee or student comprehension levels, teacher effectiveness levels, gamer skill levels, etc.).

In some embodiments, the response system 406 can be further configured to receive one or several responses from the user and analyze these one or several responses. In some embodiments, for example, the response system 406 can be configured to translate the one or several responses into one or several observables. As used herein, an observable is a characterization of a received response. In some embodiments, the translation of the one or several responses into one or several observables can include determining whether the one or several responses are correct responses, also referred to herein as desired responses, or are incorrect responses, also referred to herein as undesired responses. In some embodiments, the translation of the one or several responses into one or several observables can include characterizing the degree to which one or several responses are desired responses and/or undesired responses. In some embodiments, one or several values can be generated by the response system 406 to reflect user performance in responding to the one or several data packets. In some embodiments, these one or several values can comprise one or several scores for one or several responses and/or data packets.

In some embodiments, the response system 406 may process responses from users from automatically generated personalized aggregated content. The response system 406 may determine the type of response required and the desirability of the response. For instance, in the case of a high level of difficulty presentation of an incomplete clause, the response system 406 expects the user to respond with the proper fill-in-blank response. On a less difficult incomplete clause presentation, the response system 406 may expect a selection from a number of provided responses. On low level difficulty presentations, the response system 406 may expect a negative or affirmative response from the user indicating the user did not or did know the answer.

A content management server 102 also may include a presentation system 408. The presentation system 408 may be implemented using dedicated hardware within the content distribution network 100 (e.g., a presentation server 408), or using designated hardware and software resources within a shared content management server 102. The presentation system 408 can include a presentation engine that can be, for example, a software module running on the content delivery system.

The presentation system 408, also referred to herein as the presentation module or the presentation engine, may receive content resources from the packet selection system 402 and/or from the summary model system 404, and provide the resources to user devices 106. The presentation system 408 may determine the appropriate presentation format for the content resources based on the user characteristics and preferences, and/or the device capabilities of user devices 106. If needed, the presentation system 408 may convert the content resources to the appropriate presentation format and/or compress the content before transmission. In some embodiments, the presentation system 408 may also determine the appropriate transmission media and communication protocols for transmission of the content resources.

In some embodiments, the presentation system 408 may include specialized security and integration hardware 410, along with corresponding software components to implement the appropriate security features, content transmission and storage, to provide the supported network and client access models, and to support the performance and scalability requirements of the network 100. The security and integration layer 410 may include some or all of the security and integration components 208 discussed above in FIG. 2, and may control the transmission of content resources and other data, as well as the receipt of requests and content interactions, to and from the user devices 106, supervisor devices 110, administrative servers 116, and other devices in the network 100.

In some embodiments, presentation system 408 presents automatically generated personalized aggregated content to a user device 106. For instance if the aggregated content presented requires a multi-choice response, the incomplete clause and the test phrases as well as the extracted noun phrase may be presented to the user device. In the case of more or less difficult presentations the incomplete phrase may be presented to the user and may be followed by the extracted noun phrase.

With reference now to FIG. 5, a block diagram of an illustrative computer system is shown. The system 500 may correspond to any of the computing devices or servers of the content distribution network 100 described above, or any other computing devices described herein, and specifically can include, for example, one or several of the user devices 106, the supervisor device 110, and/or any of the servers 102, 104, 108, 112, 114, 116. In this example, computer system 500 includes processing units 504 that communicate with a number of peripheral subsystems via a bus subsystem 502. These peripheral subsystems include, for example, a storage subsystem 510, an I/O subsystem 526, and a communications subsystem 532.

Bus subsystem 502 provides a mechanism for letting the various components and subsystems of computer system 500 communicate with each other as intended. Although bus subsystem 502 is shown schematically as a single bus, alternative embodiments of the bus subsystem may utilize multiple buses. Bus subsystem 502 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. Such architectures may include, for example, an Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus, which can be implemented as a Mezzanine bus manufactured to the IEEE P1386.1 standard.

Processing unit 504, which may be implemented as one or more integrated circuits (e.g., a conventional microprocessor or microcontroller), controls the operation of computer system 500. One or more processors, including single core and/or multicore processors, may be included in processing unit 504. As shown in the figure, processing unit 504 may be implemented as one or more independent processing units 506 and/or 508 with single or multicore processors and processor caches included in each processing unit. In other embodiments, processing unit 504 may also be implemented as a quad-core processing unit or larger multicore designs (e.g., hexa-core processors, octo-core processors, ten-core processors, or greater).

Processing unit 504 may execute a variety of software processes embodied in program code, and may maintain multiple concurrently executing programs or processes. At any given time, some or all of the program code to be executed can be resident in processor(s) 504 and/or in storage subsystem 510. In some embodiments, computer system 500 may include one or more specialized processors, such as digital signal processors (DSPs), outboard processors, graphics processors, application-specific processors, and/or the like.

I/O subsystem 526 may include device controllers 528 for one or more user interface input devices and/or user interface output devices 530. User interface input and output devices 530 may be integral with the computer system 500 (e.g., integrated audio/video systems, and/or touchscreen displays), or may be separate peripheral devices which are attachable/detachable from the computer system 500. The I/O subsystem 526 may provide one or several outputs to a user by converting one or several electrical signals to the user in perceptible and/or interpretable form, and may receive one or several inputs from the user by generating one or several electrical signals based on one or several user-caused interactions with the I/O subsystem such as the depressing of a key or button, the moving of a mouse, the interaction with a touchscreen or trackpad, the interaction of a sound wave with a microphone, or the like.

Input devices 530 may include a keyboard, pointing devices such as a mouse or trackball, a touchpad or touch screen incorporated into a display, a scroll wheel, a click wheel, a dial, a button, a switch, a keypad, audio input devices with voice command recognition systems, microphones, and other types of input devices. Input devices 530 may also include three dimensional (3D) mice, joysticks or pointing sticks, gamepads and graphic tablets, and audio/visual devices such as speakers, digital cameras, digital camcorders, portable media players, webcams, image scanners, fingerprint scanners, barcode reader 3D scanners, 3D printers, laser rangefinders, and eye gaze tracking devices. Additional input devices 530 may include, for example, motion sensing and/or gesture recognition devices that enable users to control and interact with an input device through a natural user interface using gestures and spoken commands, eye gesture recognition devices that detect eye activity from users and transform the eye gestures as input into an input device, voice recognition sensing devices that enable users to interact with voice recognition systems through voice commands, medical imaging input devices, MIDI keyboards, digital musical instruments, and the like.

Output devices 530 may include one or more display subsystems, indicator lights, or non-visual displays such as audio output devices, etc. Display subsystems may include, for example, cathode ray tube (CRT) displays, flat-panel devices, such as those using a liquid crystal display (LCD) or plasma display, light-emitting diode (LED) displays, projection devices, touch screens, and the like. In general, use of the term “output device” is intended to include all possible types of devices and mechanisms for outputting information from computer system 500 to a user or other computer. For example, output devices 530 may include, without limitation, a variety of display devices that visually convey text, graphics and audio/video information such as monitors, printers, speakers, headphones, automotive navigation systems, plotters, voice output devices, and modems.

Computer system 500 may comprise one or more storage subsystems 510, comprising hardware and software components used for storing data and program instructions, such as system memory 518 and computer-readable storage media 516. The system memory 518 and/or computer-readable storage media 516 may store program instructions that are loadable and executable on processing units 504, as well as data generated during the execution of these programs.

Depending on the configuration and type of computer system 500, system memory 318 may be stored in volatile memory (such as random access memory (RAM) 512) and/or in non-volatile storage drives 514 (such as read-only memory (ROM), flash memory, etc.). The RAM 512 may contain data and/or program modules that are immediately accessible to and/or presently being operated and executed by processing units 504. In some implementations, system memory 518 may include multiple different types of memory, such as static random access memory (SRAM) or dynamic random access memory (DRAM). In some implementations, a basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within computer system 500, such as during start-up, may typically be stored in the non-volatile storage drives 514. By way of example, and not limitation, system memory 518 may include application programs 520, such as client applications, Web browsers, mid-tier applications, server applications, etc., program data 522, and an operating system 524.

Storage subsystem 510 also may provide one or more tangible computer-readable storage media 516 for storing the basic programming and data constructs that provide the functionality of some embodiments. Software (programs, code modules, instructions) that, when executed by a processor, provide the functionality described herein may be stored in storage subsystem 510. These software modules or instructions may be executed by processing units 504. Storage subsystem 510 may also provide a repository for storing data used in accordance with the present invention.

Storage subsystem 300 may also include a computer-readable storage media reader that can further be connected to computer-readable storage media 516. Together and, optionally, in combination with system memory 518, computer-readable storage media 516 may comprehensively represent remote, local, fixed, and/or removable storage devices plus storage media for temporarily and/or more permanently containing, storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 516 containing program code, or portions of program code, may include any appropriate media known or used in the art, including storage media and communication media, such as but not limited to, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage and/or transmission of information. This can include tangible computer-readable storage media such as RAM, ROM, electronically erasable programmable ROM (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible computer readable media. This can also include nontangible computer-readable media, such as data signals, data transmissions, or any other medium which can be used to transmit the desired information and which can be accessed by computer system 500.

By way of example, computer-readable storage media 516 may include a hard disk drive that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive that reads from or writes to a removable, nonvolatile magnetic disk, and an optical disk drive that reads from or writes to a removable, nonvolatile optical disk such as a CD ROM, DVD, and Blu-Ray® disk, or other optical media. Computer-readable storage media 516 may include, but is not limited to, Zip® drives, flash memory cards, universal serial bus (USB) flash drives, secure digital (SD) cards, DVD disks, digital video tape, and the like. Computer-readable storage media 516 may also include, solid-state drives (SSD) based on non-volatile memory such as flash-memory based SSDs, enterprise flash drives, solid state ROM, and the like, SSDs based on volatile memory such as solid state RAM, dynamic RAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, and hybrid SSDs that use a combination of DRAM and flash memory based SSDs. The disk drives and their associated computer-readable media may provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for computer system 500.

Communications subsystem 532 may provide a communication interface from computer system 500 and external computing devices via one or more communication networks, including local area networks (LANs), wide area networks (WANs) (e.g., the Internet), and various wireless telecommunications networks. As illustrated in FIG. 5, the communications subsystem 532 may include, for example, one or more network interface controllers (NICs) 534, such as Ethernet cards, Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as well as one or more wireless communications interfaces 536, such as wireless network interface controllers (WNICs), wireless network adapters, and the like. As illustrated in FIG. 5, the communications subsystem 532 may include, for example, one or more location determining features 538 such as one or several navigation system features and/or receivers, and the like. Additionally and/or alternatively, the communications subsystem 532 may include one or more modems (telephone, satellite, cable, ISDN), synchronous or asynchronous digital subscriber line (DSL) units, FireWire® interfaces, USB® interfaces, and the like. Communications subsystem 536 also may include radio frequency (RF) transceiver components for accessing wireless voice and/or data networks (e.g., using cellular telephone technology, advanced data network technology, such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi (IEEE 802.11 family standards, or other mobile communication technologies, or any combination thereof), global positioning system (GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 532 may be detachable components coupled to the computer system 500 via a computer network, a FireWire® bus, or the like, and/or may be physically integrated onto a motherboard of the computer system 500. Communications subsystem 532 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 532 may also receive input communication in the form of structured and/or unstructured data feeds, event streams, event updates, and the like, on behalf of one or more users who may use or access computer system 500. For example, communications subsystem 532 may be configured to receive data feeds in real-time from users of social networks and/or other communication services, web feeds such as Rich Site Summary (RSS) feeds, and/or real-time updates from one or more third party information sources (e.g., data aggregators 311). Additionally, communications subsystem 532 may be configured to receive data in the form of continuous data streams, which may include event streams of real-time events and/or event updates (e.g., sensor data applications, financial tickers, network performance measuring tools, clickstream analysis tools, automobile traffic monitoring, etc.). Communications subsystem 532 may output such structured and/or unstructured data feeds, event streams, event updates, and the like to one or more data stores 104 that may be in communication with one or more streaming data source computers coupled to computer system 500.

Due to the ever-changing nature of computers and networks, the description of computer system 500 depicted in the figure is intended only as a specific example. Many other configurations having more or fewer components than the system depicted in the figure are possible. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, firmware, software, or a combination. Further, connection to other computing devices, such as network input/output devices, may be employed. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the various embodiments.

With reference now to FIG. 6, a block diagram illustrating one embodiment of the communication network is shown. Specifically, FIG. 6 depicts one hardware configuration in which messages are exchanged between a source hub 602 via the communication network 120 that can include one or several intermediate hubs 604. In some embodiments, the source hub 602 can be any one or several components of the content distribution network generating and initiating the sending of a message, and the terminal hub 606 can be any one or several components of the content distribution network 100 receiving and not re-sending the message. In some embodiments, for example, the source hub 602 can be one or several of the user device 106, the supervisor device 110, and/or the server 102, and the terminal hub 606 can likewise be one or several of the user device 106, the supervisor device 110, and/or the server 102. In some embodiments, the intermediate hubs 604 can include any computing device that receives the message and resends the message to a next node.

As seen in FIG. 6, in some embodiments, each of the hubs 602, 604, 606 can be communicatingly connected with the data store 104. In such an embodiments, some or all of the hubs 602, 604, 606 can send information to the data store 104 identifying a received message and/or any sent or resent message. This information can, in some embodiments, be used to determine the completeness of any sent and/or received messages and/or to verify the accuracy and completeness of any message received by the terminal hub 606.

In some embodiments, the communication network 120 can be formed by the intermediate hubs 604. In some embodiments, the communication network 120 can comprise a single intermediate hub 604, and in some embodiments, the communication network 120 can comprise a plurality of intermediate hubs. In one embodiment, for example, and as depicted in FIG. 6, the communication network 120 includes a first intermediate hub 604-A and a second intermediate hub 604-B.

With reference now to FIG. 7, a block diagram illustrating one embodiment of user device 106 and supervisor device 110 communication is shown. In some embodiments, for example, a user may have multiple devices that can connect with the content distribution network 100 to send or receive information. In some embodiments, for example, a user may have a personal device such as a mobile device, a Smartphone, a tablet, a Smartwatch, a laptop, a PC, or the like. In some embodiments, the other device can be any computing device in addition to the personal device. This other device can include, for example, a laptop, a PC, a Smartphone, a tablet, a Smartwatch, or the like. In some embodiments, the other device differs from the personal device in that the personal device is registered as such within the content distribution network 100 and the other device is not registered as a personal device within the content distribution network 100.

Specifically with respect to FIG. 7, the user device 106 can include a personal user device 106-A and one or several other user devices 106-B. In some embodiments, one or both of the personal user device 106-A and the one or several other user devices 106-B can be communicatingly connected to the content management server 102 and/or to the navigation system 122. Similarly, the supervisor device 110 can include a personal supervisor device 110-A and one or several other supervisor devices 110-B. In some embodiments, one or both of the personal supervisor device 110-A and the one or several other supervisor devices 110-B can be communicatingly connected to the content management server 102 and/or to the navigation system 122.

In some embodiments, the content distribution network can send one or more alerts to one or more user devices 106 and/or one or more supervisor devices 110 via, for example, the communication network 120. In some embodiments, the receipt of the alert can result in the launching of an application within the receiving device, and in some embodiments, the alert can include a link that, when selected, launches the application or navigates a web-browser of the device of the selector of the link to a page or portal associated with the alert.

In some embodiments, for example, the providing of this alert can include the identification of one or several user devices 106 and/or student-user accounts associated with the student-user and/or one or several supervisor devices 110 and/or supervisor-user accounts associated with the supervisor-user. After these one or several devices 106, 110 and/or accounts have been identified, the providing of this alert can include determining an active device of the devices 106, 110 based on determining which of the devices 106, 110 and/or accounts are actively being used, and then providing the alert to that active device.

Specifically, if the user is actively using one of the devices 106, 110 such as the other user device 106-B and the other supervisor device 110-B, and/or accounts, the alert can be provided to the user via that other device 106-B, 110-B and/or account that is actively being used. If the user is not actively using another device 106-B, 110-B and/or account, a personal device 106-A, 110-A device, such as a smart phone or tablet, can be identified and the alert can be provided to this personal device 106-A, 110-A. In some embodiments, the alert can include code to direct the default device to provide an indicator of the received alert such as, for example, an aural, tactile, or visual indicator of receipt of the alert.

In some embodiments, the recipient device 106, 110 of the alert can provide an indication of receipt of the alert. In some embodiments, the presentation of the alert can include the control of the I/O subsystem 526 to, for example, provide an aural, tactile, and/or visual indicator of the alert and/or of the receipt of the alert. In some embodiments, this can include controlling a screen of the supervisor device 110 to display the alert, data contained in alert and/or an indicator of the alert.

With reference now to FIG. 8, a schematic illustration of one embodiment of an application stack, and particularly of a stack 650 is shown. In some embodiments, the content distribution network 100 can comprise a portion of the stack 650 that can include an infrastructure layer 652, a platform layer 654, an applications layer 656, and a products layer 658. In some embodiments, the stack 650 can comprise some or all of the layers, hardware, and/or software to provide one or several desired functionalities and/or productions.

As depicted in FIG. 8, the infrastructure layer 652 can include one or several servers, communication networks, data stores, privacy servers, and the like. In some embodiments, the infrastructure layer can further include one or several user devices 106 and/or supervisor devices 110 connected as part of the content distribution network.

The platform layer can include one or several platform software programs, modules, and/or capabilities. These can include, for example, identification services, security services, and/or adaptive platform services 660. In some embodiments, the identification services can, for example, identify one or several users, components of the content distribution network 100, or the like. The security services can monitor the content distribution network for one or several security threats, breaches, viruses, malware, or the like. The adaptive platform services 660 can receive information from one or several components of the content distribution network 100 and can provide predictions, models, recommendations, or the like based on that received information. The functionality of the adaptive platform services 660 will be discussed in greater detail in FIGS. 9A-9C, below.

The applications layer 656 can include software or software modules upon or in which one or several product softwares or product software modules can operate. In some embodiments, the applications layer 656 can include, for example, a management system, record system, or the like. In some embodiments, the management system can include, for example, a Learning Management System (LMS), a Content Management System (CMS), or the like. The management system can be configured to control the delivery of one or several resources to a user and/or to receive one or several responses from the user. In some embodiments, the records system can include, for example, a virtual gradebook, a virtual counselor, or the like.

The products layer 658 can include one or several software products and/or software module products. These software products and/or software module products can provide one or several services and/or functionalities to one or several users of the software products and/or software module products.

With reference now to FIG. 9A-9C, schematic illustrations of embodiments of communication and processing flow of modules within the content distribution network 100 are shown. In some embodiments, the communication and processing can be performed in portions of the platform layer 654 and/or applications layer 656. FIG. 9A depicts a first embodiment of such communications or processing that can be in the platform layer 654 and/or applications layer 656 via the message channel 412.

The platform layer 654 and/or applications layer 656 can include a plurality of modules that can be embodied in software or hardware. In some embodiments, some or all of the modules can be embodied in hardware and/or software at a single location, and in some embodiments, some or all of these modules can be embodied in hardware and/or software at multiple locations. These modules can perform one or several processes including, for example, a presentation process 670, a response process 676, a summary model process 680, and a packet selection process 684.

The presentation process 670 can, in some embodiments, include one or several methods and/or steps to deliver content to one or several user devices 106 and/or supervisor devices 110. The presentation process 670 can be performed by a presenter module 672 and a view module 674. The presenter module 672 can be a hardware or software module of the content distribution network 100, and specifically of the server 102. In some embodiments, the presenter module 672 can include one or several portions, features, and/or functionalities that are located on the server 102 and/or one or several portions, features, and/or functionalities that are located on the user device 106. In some embodiments, the presenter module 672 can be embodied in the presentation system 408.

The presenter module 672 can control the providing of content to one or several user devices 106 and/or supervisor devices 110. Specifically, the presenter module 672 can control the generation of one or several messages to provide content to one or several desired user devices 106 and/or supervisor devices 110. The presenter module 672 can further control the providing of these one or several messages to the desired one or several desired user devices 106 and/or supervisor devices 110. Thus, in some embodiments, the presenter module 672 can control one or several features of the communications subsystem 532 to generate and send one or several electrical signals comprising content to one or several user devices 106 and/or supervisor devices 110.

In some embodiments, the presenter module 672 can control and/or manage a portion of the presentation functions of the presentation process 670, and can specifically manage an “outer loop” of presentation functions. As used herein, the outer loop refers to tasks relating to the tracking of a user's progress through all or a portion of a group of data packets. In some embodiments, this can include the identification of one or several completed data packets or nodes and/or the non-adaptive selection of one or several next data packets or nodes according to, for example, one or several fixed rules. Such non-adaptive selection does not rely on the use of predictive models, but rather on rules identifying next data packets based on data relating to the completion of one or several previously completed data packets or assessments and/or whether one or several previously completed data packets were successfully completed.

In some embodiments, and due to the management of the outer loop of presentation functions including the non-adaptive selection of one or several next data packets, nodes, or tasks by the presenter module, the presenter module can function as a recommendation engine referred to herein as a first recommendation engine or a rules-based recommendation engine. In some embodiments, the first recommendation engine can be configured to select a next node for a user based on one or all of: the user's current location in the content network; potential next nodes; the user's history including the user's previous responses; and one or several guard conditions associated with the potential next nodes. In some embodiments, a guard condition defines one or several prerequisites for entry into, or exit from, a node.

In some embodiments, the presenter module 672 can include a portion located on the server 102 and/or a portion located on the user device 106. In some embodiments, the portion of the presenter module 672 located on the server 102 can receive data packet information and provide a subset of the received data packet information to the portion of the presenter module 672 located on the user device 106. In some embodiments, this segregation of functions and/or capabilities can prevent solution data from being located on the user device 106 and from being potentially accessible by the user of the user device 106.

In some embodiments, the portion of the presenter module 672 located on the user device 106 can be further configured to receive the subset of the data packet information from the portion of the presenter module 672 located on the server 102 and provide that subset of the data packet information to the view module 674. In some embodiments, the portion of the presenter module 672 located on the user device 106 can be further configured to receive a content request from the view module 674 and to provide that content request to the portion of the presenter module 674 located on the server 102.

The view module 674 can be a hardware or software module of some or all of the user devices 106 and/or supervisor devices 110 of the content distribution network 100. The view module 674 can receive one or several electrical signals and/or communications from the presenter module 672 and can provide the content received in those one or several electrical signals and/or communications to the user of the user device 106 and/or supervisor device 110 via, for example, the I/O subsystem 526.

In some embodiments, the view module 674 can control and/or monitor an “inner loop” of presentation functions. As used herein, the inner loop refers to tasks relating to the tracking and/or management of a user's progress through a data packet. This can specifically relate to the tracking and/or management of a user's progression through one or several pieces of content, questions, assessments, and/or the like of a data packet. In some embodiments, this can further include the selection of one or several next pieces of content, next questions, next assessments, and/or the like of the data packet for presentation and/or providing to the user of the user device 106.

In some embodiments, one or both of the presenter module 672 and the view module 674 can comprise one or several presentation engines. In some embodiments, these one or several presentation engines can comprise different capabilities and/or functions. In some embodiments, one of the presentation engines can be configured to track the progress of a user through a single data packet, task, content item, or the like, and in some embodiments, one of the presentation engines can track the progress of a user through a series of data packets, tasks, content items, or the like.

The response process 676 can comprise one or several methods and/or steps to evaluate a response. In some embodiments, this can include, for example, determining whether the response comprises a desired response and/or an undesired response. In some embodiments, the response process 676 can include one or several methods and/or steps to determine the correctness and/or incorrectness of one or several received responses. In some embodiments, this can include, for example, determining the correctness and/or incorrectness of a multiple choice response, a true/false response, a short answer response, an essay response, or the like. In some embodiments, the response processor can employ, for example, natural language processing, semantic analysis, or the like in determining the correctness or incorrectness of the received responses.

In some embodiments, the response process 676 can be performed by a response processor 678. The response processor 678 can be a hardware or software module of the content distribution network 100, and specifically of the server 102. In some embodiments, the response processor 678 can be embodied in the response system 406. In some embodiments, the response processor 678 can be communicatingly connected to one or more of the modules of the presentation process 760 such as, for example, the presenter module 672 and/or the view module 674. In some embodiments, the response processor 678 can be communicatingly connected with, for example, the message channel 412 and/or other components and/or modules of the content distribution network 100.

The summary model process 680 can comprise one or several methods and/or steps to generate and/or update one or several models. In some embodiments, this can include, for example, implementing information received either directly or indirectly from the response processor 678 to update one or several models. In some embodiments, the summary model process 680 can include the update of a model relating to one or several user attributes such as, for example, a user skill model, a user knowledge model, a learning style model, or the like. In some embodiments, the summary model process 680 can include the update of a model relating to one or several content attributes including attributes relating to a single content item and/or data packet and/or attributes relating to a plurality of content items and/or data packets. In some embodiments, these models can relate to an attribute of the one or several data packets such as, for example, difficulty, discrimination, required time, or the like.

In some embodiments, the summary model process 680 can be performed by the model engine 682. In some embodiments, the model engine 682 can be a hardware or software module of the content distribution network 100, and specifically of the server 102. In some embodiments, the model engine 682 can be embodied in the summary model system 404.

In some embodiments, the model engine 682 can be communicatingly connected to one or more of the modules of the presentation process 760 such as, for example, the presenter module 672 and/or the view module 674, can be connected to the response processor 678 and/or the recommendation. In some embodiment, the model engine 682 can be communicatingly connected to the message channel 412 and/or other components and/or modules of the content distribution network 100.

The packet selection process 684 can comprise one or several steps and/or methods to identify and/or select a data packet for presentation to a user. In some embodiments, this data packet can comprise a plurality of data packets. In some embodiments, this data packet can be selected according to one or several models updated as part of the summary model process 680. In some embodiments, this data packet can be selected according to one or several rules, probabilities, models, or the like. In some embodiments, the one or several data packets can be selected by the combination of a plurality of models updated in the summary model process 680 by the model engine 682. In some embodiments, these one or several data packets can be selected by a recommendation engine 686. The recommendation engine 686 can be a hardware or software module of the content distribution network 100, and specifically of the server 102. In some embodiments, the recommendation engine 686 can be embodied in the packet selection system 402. In some embodiments, the recommendation engine 686 can be communicatingly connected to one or more of the modules of the presentation process 670, the response process 676, and/or the summary model process 680 either directly and/or indirectly via, for example, the message channel.

In some embodiments, and as depicted in FIG. 9A, a presenter module 672 can receive a data packet for presentation to a user device 106. This data packet can be received, either directly or indirectly from a recommendation engine 686. In some embodiments, for example, the presenter module 672 can receive a data packet for providing to a user device 106 from the recommendation engine 686, and in some embodiments, the presenter module 672 can receive an identifier of a data packet for providing to a user device 106 via a view module 674. This can be received from the recommendation engine 686 via a message channel 412. Specifically, in some embodiments, the recommendation engine 686 can provide data to the message channel 412 indicating the identification and/or selection of a data packet for providing to a user via a user device 106. In some embodiments, this data indicating the identification and/or selection of the data packet can identify the data packet and/or can identify the intended recipient of the data packet.

The message channel 412 can output this received data in the form of a data stream 690 which can be received by, for example, the presenter module 672, the model engine 682, and/or the recommendation engine 686. In some embodiments, some or all of: the presenter module 672, the model engine 682, and/or the recommendation engine 686 can be configured to parse and/or filter the data stream 690 to identify data and/or events relevant to their operation. Thus, for example, the presenter module 672 can be configured to parse the data stream for information and/or events relevant to the operation of the presenter module 672.

In some embodiments, the presenter module 672 can extract the data packet from the data stream 690 and/or extract data identifying the data packet and/or indicating the selecting of a data packet from the data stream. In the event that data identifying the data packet is extracted from the data stream 690, the presenter module 672 can request and receive the data packet from the database server 104, and specifically from the content library database 303. In embodiments in which data indicating the selection of a data packet is extracted from the data stream 690, the presenter module 672 can request and receive identification of the data packet from the recommendation engine 686 and then request and receive the data packet from the database server 104, and specifically from the content library database 303, and in some embodiments in which data indicating the selection of a data packet is extracted from the data stream 690, the presenter module 672 can request and receive the data packet from the recommendation engine 686.

The presenter module can then provide the data packet and/or portions of the data packet to the view module 674. In some embodiments, for example, the presenter module 672 can retrieve one or several rules and/or conditions that can be, for example, associated with the data packet and/or stored in the database server 104. In some embodiments, these rules and/or conditions can identify portions of a data packet for providing to the view module 674 and/or portions of a data packet to not provide to the view module 674. In some embodiments, for example, sensitive portions of a data packet, such as, for example, solution information to any questions associated with a data packet, is not provided to the view module 674 to prevent the possibility of undesired access to those sensitive portions of the data packet. Thus, in some embodiments, the one or several rules and/or conditions can identify portions of the data packet for providing to the view module 674 and/or portions of the data packet for not providing to the view module.

In some embodiments, the presenter module 672 can, according to the one or more rules and/or conditions, generate and transmit an electronic message containing all or portions of the data packet to the view module 674. The view module 674 can receive these all or portions of the data packet and can provide all or portions of this information to the user of the user device 106 associated with the view module 674 via, for example, the I/O subsystem 526. In some embodiments, as part of the providing of all or portions of the data packet to the user of the view module 674, one or several user responses can be received by the view module 674. In some embodiments, these one or several user responses can be received via the I/O subsystem 526 of the user device 106.

After one or several user responses have been received, the view module 674 can provide the one or several user responses to the response processor 678. In some embodiments, these one or several responses can be directly provided to the response processor 678, and in some embodiments, these one or several responses can be provided indirectly to the response processor 678 via the message channel 412.

After the response processor 678 receives the one or several responses, the response processor 678 can determine whether the responses are desired responses and/or the degree to which the received responses are desired responses. In some embodiments, the response processor can make this determination via, for example, use of one or several techniques, including, for example, natural language processing (NLP), semantic analysis, or the like.

In some embodiments, the response processor can determine whether a response is a desired response and/or the degree to which a response is a desired response with comparative data which can be associated with the data packet. In some embodiments, this comparative data can comprise, for example, an indication of a desired response and/or an indication of one or several undesired responses, a response key, a response rubric comprising one criterion or several criteria for determining the degree to which a response is a desired response, or the like. In some embodiments, the comparative data can be received as a portion of and/or associated with a data packet. In some embodiments, the comparative data can be received by the response processor 678 from the presenter module 672 and/or from the message channel 412. In some embodiments, the response data received from the view module 674 can comprise data identifying the user and/or the data packet or portion of the data packet with which the response is associated. In some embodiments in which the response processor 678 merely receives data identifying the data packet and/or portion of the data packet associated with the one or several responses, the response processor 678 can request and/or receive comparative data from the database server 104, and specifically from the content library database 303 of the database server 104.

After the comparative data has been received, the response processor 678 determines whether the one or several responses comprise desired responses and/or the degree to which the one or several responses comprise desired responses. The response processor can then provide the data characterizing whether the one or several responses comprise desired responses and/or the degree to which the one or several responses comprise desired responses to the message channel 412. The message channel can, as discussed above, include the output of the response processor 678 in the data stream 690 which can be constantly output by the message channel 412.

In some embodiments, the model engine 682 can subscribe to the data stream 690 of the message channel 412 and can thus receive the data stream 690 of the message channel 412 as indicated in FIG. 9A. The model engine 682 can monitor the data stream 690 to identify data and/or events relevant to the operation of the model engine. In some embodiments, the model engine 682 can monitor the data stream 690 to identify data and/or events relevant to the determination of whether a response is a desired response and/or the degree to which a response is a desired response.

When a relevant event and/or relevant data are identified by the model engine, the model engine 682 can take the identified relevant event and/or relevant data and modify one or several models. In some embodiments, this can include updating and/or modifying one or several models relevant to the user who provided the responses, updating and/or modifying one or several models relevant to the data packet associated with the responses, and/or the like. In some embodiments, these models can be retrieved from the database server 104, and, in some embodiments, can be retrieved from the model data source 309 of the database server 104.

After the models have been updated, the updated models can be stored in the database server 104. In some embodiments, the model engine 682 can send data indicative of the event of the completion of the model update to the message channel 412. The message channel 412 can incorporate this information into the data stream 690 which can be received by the recommendation engine 686. The recommendation engine 686 can monitor the data stream 690 to identify data and/or events relevant to the operation of the recommendation engine 686. In some embodiments, the recommendation engine 686 can monitor the data stream 690 to identify data and/or events relevant to the updating of one or several models by the model engine 682.

When the recommendation engine 686 identifies information in the data stream 690 indicating the completion of the summary model process 680 for models relevant to the user providing the response and/or for models relevant to the data packet provided to the user, the recommendation engine 686 can identify and/or select a next data packet for providing to the user and/or to the presentation process 470. In some embodiments, this selection of the next data packet can be performed according to one or several rules and/or conditions. After the next data packet has been selected, the recommendation engine 686 can provide information to the model engine 682 identifying the next selected data packet and/or to the message channel 412 indicating the event of the selection of the next content item. After the message channel 412 receives information identifying the selection of the next content item and/or receives the next content item, the message channel 412 can include this information in the data stream 690 and the process discussed with respect to FIG. 9A can be repeated.

With reference now to FIG. 9B, a schematic illustration of a second embodiment of communication or processing that can be in the platform layer 654 and/or applications layer 656 via the message channel 412 is shown. In the embodiment depicted in FIG. 9B, the data packet provided to the presenter module 672 and then to the view module 674 does not include a prompt for a user response and/or does not result in the receipt of a user response. As no response is received, when the data packet is completed, nothing is provided to the response processor 678, but rather data indicating the completion of the data packet is provided from one of the view module 674 and/or the presenter module 672 to the message channel 412. The data is then included in the data stream 690 and is received by the model engine 682 which uses the data to update one or several models. After the model engine 682 has updated the one or several models, the model engine 682 provides data indicating the completion of the model updates to the message channel 412. The message channel 412 then includes the data indicating the completion of the model updates in the data stream 690 and the recommendation engine 686, which can subscribe to the data stream 690, can extract the data indicating the completion of the model updates from the data stream 690. The recommendation engine 686 can then identify a next one or several data packets for providing to the presenter module 672, and the recommendation engine 686 can then, either directly or indirectly, provide the next one or several data packets to the presenter module 672.

In some embodiments, of the communication as shown in FIGS. 9A and 9B, all communications between any of the presenter module 672, the response processor 678, the model engine 682, and the recommendation engine 686 can pass through the message channel 412. Alternatively, in some embodiments, some of the communications between any of the presenter module 672, the response processor 678, the model engine 682, and the recommendation engine 686 can pass through the message channel and others of the communications between any of the presenter module 672, the response processor 678, the model engine 682, and the recommendation engine 686 can be direct.

With reference now to FIG. 9C, a schematic illustration of an embodiment of dual communication, or hybrid communication, in the platform layer 654 and/or applications layer 656 is shown. Specifically, in this embodiment, some communication is synchronous with the completion of one or several tasks and some communication is asynchronous. Thus, in the embodiment depicted in FIG. 9C, the presenter module 972 communicates synchronously with the model engine 682 via a direct communication 692 and communicates asynchronously with the model engine 682 via the message channel 412.

In some embodiments, and as depicted in FIG. 9C, the synchronous communication and/or the operation of the presenter module 672, the response processor 678, the model engine 682, and the recommendation engine 686 can be directed and/or controlled by a controller. In some embodiments, this controller can be part of the server 102 and/or located in any one or more of the presenter module 672, the response processor 678, the model engine 682, and the recommendation engine 686. In some embodiments, this controller can be located in the presenter module 672, which presenter module can control communications with and between itself and the response processor 678, the model engine 682, and the recommendation engine 686, and the presenter module can thus control the functioning of the response processor 678, the model engine 682, and the recommendation engine 686.

Specifically, and with reference to FIG. 9C, the presenter module 672 can receive and/or select a data packet for presentation to the user device 106 via the view module 674. In some embodiments, the presenter module 672 can identify all or portions of the data packet that can be provided to the view module 674 and portions of the data packet for retaining from the view module 674. In some embodiments, the presenter module can provide all or portions of the data packet to the view module 674. In some embodiments, and in response to the receipt of all or portions of the data packet, the view module 674 can provide a confirmation of receipt of the all or portions of the data packet and can provide those all or portions of the data packet to the user via the user device 106. In some embodiments, the view module 674 can provide those all or portions of the data packet to the user device 106 while controlling the inner loop of the presentation of the data packet to the user via the user device 106.

After those all or portions of the data packet have been provided to the user device 106, a response indicative of the completion of one or several tasks associated with the data packet can be received by the view module 674 from the user device 106, and specifically from the I/O subsystem 526 of the user device 106. In response to this receive, the view module 674 can provide an indication of this completion status to the presenter module 672 and/or can provide the response to the response processor 678.

After the response has been received by the response processor 678, the response processor 678 can determine whether the received response is a desired response. In some embodiments, this can include, for example, determining whether the response comprises a correct answer and/or the degree to which the response comprises a correct answer.

After the response processor has determined whether the received response is a desired response, the response processor 678 can provide an indicator of the result of the determination of whether the received response is a desired response to the presenter module 672. In response to the receipt of the indicator of whether the result of the determination of whether the received response is a desired response, the presenter module 672 can synchronously communicate with the model engine 682 via a direct communication 692 and can asynchronously communicate with model engine 682 via the message channel 412. In some embodiments, the synchronous communication can advantageously include two-way communication between the model engine 682 and the presenter module 672 such that the model engine 682 can provide an indication to the presenter module 672 when model updating is completed by the model engine.

After the model engine 682 has received one or both of the synchronous and asynchronous communications, the model engine 682 can update one or several models relating to, for example, the user, the data packet, or the like. After the model engine 682 has completed the updating of the one or several models, the model engine 682 can send a communication to the presenter module 672 indicating the completion of the updated one or several modules.

After the presenter module 672 receives the communication indicating the completion of the updating of the one or several models, the presenter module 672 can send a communication to the recommendation engine 686 requesting identification of a next data packet. As discussed above, the recommendation engine 686 can then retrieve the updated model and retrieve the user information. With the updated models and the user information, the recommendation engine can identify a next data packet for providing to the user, and can provide the data packet to the presenter module 672. In some embodiments, the recommendation engine 686 can further provide an indication of the next data packet to the model engine 682, which can use this information relating to the next data packet to update one or several models, either immediately, or after receiving a communication from the presenter module 672 subsequent to the determination of whether a received response for that data packet is a desired response.

With reference now to FIG. 9D, a schematic illustration of one embodiment of the presentation process 670 is shown. Specifically, FIG. 9D depicts multiple portions of the presenter module 672, namely, the external portion 673 and the internal portion 675. In some embodiments, the external portion 673 of the presenter module 672 can be located in the server, and in some embodiments, the internal portion 675 of the presenter module 672 can be located in the user device 106. In some embodiments, the external portion 673 of the presenter module can be configured to communicate and/or exchange data with the internal portion 675 of the presenter module 672 as discussed herein. In some embodiments, for example, the external portion 673 of the presenter module 672 can receive a data packet and can parse the data packet into portions for providing to the internal portion 675 of the presenter module 672 and portions for not providing to the internal portion 675 of the presenter module 672. In some embodiments, the external portion 673 of the presenter module 672 can receive a request for additional data and/or an additional data packet from the internal portion 675 of the presenter module 672. In such an embodiment, the external portion 673 of the presenter module 672 can identify and retrieve the requested data and/or the additional data packet from, for example, the database server 104 and more specifically from the content library database 104.

With reference now to FIG. 10A, a flowchart illustrating one embodiment of a process 440 for data management is shown. In some embodiments, the process 440 can be performed by the content management server 102, and more specifically by the presentation system 408 and/or by the presentation module or presentation engine. In some embodiments, the process 440 can be performed as part of the presentation process 670.

The process 440 begins at block 442, wherein a data packet is identified. In some embodiments, the data packet can be a data packet for providing to a student-user. In some embodiments, the data packet can be identified based on a communication received either directly or indirectly from the recommendation engine 686.

After the data packet has been identified, the process 440 proceeds to block 444, wherein the data packet is requested. In some embodiments, this can include the requesting of information relating to the data packet such as the data forming the data packet. In some embodiments, this information can be requested from, for example, the content library database 303. After the data packet has been requested, the process 440 proceeds to block 446, wherein the data packet is received. In some embodiments, the data packet can be received by the presentation system 408 from, for example, the content library database 303.

After the data packet has been received, the process 440 proceeds to block 448, wherein one or several data components are identified. In some embodiments, for example, the data packet can include one or several data components which can, for example, contain different data. In some embodiments, one of these data components, referred to herein as a presentation component, can include content for providing to the student user, which content can include one or several requests and/or questions and/or the like. In some embodiments, one of these data components, referred to herein as a response component, can include data used in evaluating one or several responses received from the user device 106 in response to the data packet, and specifically in response to the presentation component and/or the one or several requests and/or questions of the presentation component. Thus, in some embodiments, the response component of the data packet can be used to ascertain whether the user has provided a desired response or an undesired response.

After the data components have been identified, the process 440 proceeds to block 450, wherein a delivery data packet is identified. In some embodiments, the delivery data packet can include the one or several data components of the data packets for delivery to a user such as the student-user via the user device 106. In some embodiments, the delivery packet can include the presentation component, and in some embodiments, the delivery packet can exclude the response packet. After the delivery data packet has been generated, the process 440 proceeds to block 452, wherein the delivery data packet is provided to the user device 106 and more specifically to the view module 674. In some embodiments, this can include providing the delivery data packet to the user device 106 via, for example, the communication network 120.

After the delivery data packet has been provided to the user device 106, the process 440 proceeds to block 454, wherein the data packet and/or one or several components thereof are sent to and/or provided to the response processor 678. In some embodiments, this sending of the data packet and/or one or several components thereof to the response processor can include receiving a response from the student-user, and sending the response to the student-user to the response processor simultaneous with the sending of the data packet and/or one or several components thereof to the response processor. In some embodiments, for example, this can include providing the response component to the response processor. In some embodiments, the response component can be provided to the response processor from the presentation system 408.

With reference now to FIG. 10B, a flowchart illustrating one embodiment of a process 460 for evaluating a response is shown. In some embodiments, the process can be performed as a part of the response process 676 and can be performed by, for example, the response system 406 and/or by the response processor 678. In some embodiments, the process 460 can be performed by the response system 406 in response to the receipt of a response, either directly or indirectly, from the user device 106 or from the view module 674.

The process 460 begins at block 462, wherein a response is received from, for example, the user device 106 via, for example, the communication network 120. After the response has been received, the process 460 proceeds to block 464, wherein the data packet associated with the response is received. In some embodiments, this can include receiving all or one or several components of the data packet such as, for example, the response component of the data packet. In some embodiments, the data packet can be received by the response processor from the presentation engine.

After the data packet has been received, the process 460 proceeds to block 466, wherein the response type is identified. In some embodiments, this identification can be performed based on data, such as metadata associated with the response. In other embodiments, this identification can be performed based on data packet information such as the response component.

In some embodiments, the response type can identify one or several attributes of the one or several requests and/or questions of the data packet such as, for example, the request and/or question type. In some embodiments, this can include identifying some or all of the one or several requests and/or questions as true/false, multiple choice, short answer, essay, or the like.

After the response type has been identified, the process 460 proceeds to block 468, wherein the data packet and the response are compared to determine whether the response comprises a desired response and/or an undesired response. In some embodiments, this can include comparing the received response and the data packet to determine if the received response matches all or portions of the response component of the data packet, to determine the degree to which the received response matches all or portions of the response component, to determine the degree to which the receive response embodies one or several qualities identified in the response component of the data packet, or the like. In some embodiments, this can include classifying the response according to one or several rules. In some embodiments, these rules can be used to classify the response as either desired or undesired. In some embodiments, these rules can be used to identify one or several errors and/or misconceptions evidenced in the response. In some embodiments, this can include, for example: use of natural language processing software and/or algorithms; use of one or several digital thesauruses; use of lemmatization software, dictionaries, and/or algorithms; or the like.

After the data packet and the response have been compared, the process 460 proceeds to block 470 wherein response desirability is determined. In some embodiments this can include, based on the result of the comparison of the data packet and the response, whether the response is a desired response or is an undesired response. In some embodiments, this can further include quantifying the degree to which the response is a desired response. This determination can include, for example, determining if the response is a correct response, an incorrect response, a partially correct response, or the like. In some embodiments, the determination of response desirability can include the generation of a value characterizing the response desirability and the storing of this value in one of the databases 104 such as, for example, the user profile database 301. After the response desirability has been determined, the process 460 proceeds to block 472, wherein an assessment value is generated. In some embodiments, the assessment value can be an aggregate value characterizing response desirability for one or more a plurality of responses. This assessment value can be stored in one of the databases 104 such as the user profile database 301.

With reference now to FIG. 11, a flowchart illustrating one embodiment of a process 700 for automated aggregation delivery is shown. The process 700 can be performed by some or all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. The process 700 begins at block 702 wherein a user identifier is received and/or retrieved. In some embodiments, the user identifier can be received from the user device 106 and/or from the supervisor device 110. In some embodiments, for example, the user identifier can be provided to the user device 106 and/or supervisor device 110 via the I/O subsystem 526 and can be communicated to the server 102 via the communications subsystem 532 and the communication network 120. In some embodiments, the user identifier can comprise a user ID, password, a username, or the like.

After the user identifier has been received, the process 700 proceeds to block 704 wherein user data is retrieved. In some embodiments, this can include, for example, identifying the user associated with the user identifier, and retrieving user data associated with that user from one of the data stores 104, and specifically, in some embodiments, from the user profile data store 301.

After the user data has been retrieved, the process 700 proceeds to block 706 wherein user task data is retrieved and/or received. In some embodiments, the user task data can specify one or several tasks for the user to begin and/or complete. In some embodiments, the user tasks data can identify one or several skills, skill levels, or the like for the successful completion of the one or several tasks. In some embodiments, these one or several skills, skill levels, or the like can correspond to: skills required to successfully complete the task; a difficulty or difficulty level of the task; or the like. In some embodiments, such one or several tasks can correspond to a single data packet and in some embodiments, such one or several tasks can correspond to a plurality of data packets. In some embodiments, the task can relate to the content of one or several data packets but can be an experience or event that is not embodied in a data packet. In some embodiments, the task can be determined by the server 102 by retrieving information from the content library data store 303 identifying one or several tasks such as, for example, a schedule and/or syllabus. In some embodiments, the server 102 can determine the user's progress through the schedule and/or syllabus and can identify a current incompletion task and/or the next task. In some embodiments, the server 102 can then retrieve data relevant to that determined task which data can identify, for example, one or several content objects relevant and/or related to that task. In some embodiments, these one or several content objects relevant and/or related to the task or data associated with those one or several content objects can comprise the user task data.

In some embodiments, the user task data can be retrieved from the content library database 303 based on one or several user inputs identifying one or several tasks or objectives. In some embodiments, for example, the user can provide an input to the user device 106 identifying one or several tasks for which content aggregation delivery is desired. In response to this received input, the server 102 can query the content library database 303 for information relating to the identified one or several tasks. The server 102 can then retrieve data relevant to that determined task which data can identify, for example, one or several content objects relevant and/or related to that task. In some embodiments, these one or several content objects relevant and/or related to the task or data associated with those one or several content objects can comprise the user task data.

In some embodiments, the retrieval of user task data can further include identifying one or several skills and/or skill levels relevant to the identified task. In some embodiments, for example, these relevant skills and/or skill levels can be determined based on the one or several content items relevant to and/or related to the task. In some embodiments, these relevant skills and/or skill levels can be determined based on information associated with the task such as, for example, task metadata.

After the user task data has been retrieved, the process 700 proceeds to block 708 wherein a user time constraint parameter is received and/or retrieved. In some embodiments, user time constraint parameter can identify, for example, the amount of time before the required start or completion of the relevant task identified at block 708. In some embodiments, the user time constraint parameter can be absolute and can track the physical amount of time between the time of performing block 708 as determined by one or several clocks or timestamps associated with the server 102 and/or data received by the server 102 and the time for the start or completion of the task identified at block 706. In some embodiments, the user time constraint parameter can comprise data identifying a user defined amount of available time such as, for example, an amount of time the user can immediately dedicate to the task and/or to preparation for the task. In such an embodiment, the user can define the amount of available time via an input at the user device 106 that can be provided to the server 102. In some embodiments, the user time constraint parameter can be based on historical time usage information by the user of the content distribution network 100. In some embodiments, for example, the server 102 can retrieve information from the user profile data store 301 identifying the amount of time the user has interacted with the content distribution network 100 and during a period of time such as, for example, during the past one or several days, weeks, months, and/or years. The content distribution network 102 can further, based on other information relevant to the user and contained in the user profile data store 301 such as, for example, information identifying other tasks for completion by the user, determine an estimated amount of user available time for the task identified at block 706.

After the user time constraint parameter has been received, the process 700 proceeds to block 710 wherein user mastery data is received and/or retrieved. In some embodiments, this mastery data can identify one or several skills and/or skill levels of the user. In some embodiments, the identified one or several skills and/or skill levels can correspond to the one or several skills and/or skill levels associated with the task and that were determined at block 706. In some embodiment, the master data can be retrieved from the database server 104, and specifically can be retrieved from the user profile data store 301.

After the master data has been received and/or retrieved, the process 700 proceeds to block 712 wherein presentation data is selected. In some embodiments, the presentation data can comprise one or several content aggregations for presentation to the user. In some embodiments, these content aggregations can be generated by the user and/or can be generated from a source other than the user. In some embodiments, these one or several content aggregations can be associated with the user and can be stored in the database server 104 and specifically in the user profile database store 301 and/or the content library data store 303. In some embodiments, the presentation data can be selected to maximize user preparation for the task identified at block 706, and specifically to increase the user's one or several skills and/or skill levels that most greatly differ from the one or several skills and/or skill levels associated with the task.

After the presentation data has been selected, the process 700 proceeds to block 714, wherein the order of the presentation data is determined. In some embodiments, the order of the presentation data can comprise an order in which the content aggregations are presented to the user and/or a frequency with which the content aggregations are presented to the user. In some embodiments, the frequency with which one or several of the content aggregations are repeatedly presented to the user can include a desired time interval between repeated presentations of one or several content aggregations and/or a number of desired intermediately presented content aggregations before one or several of the content aggregations are repeatedly presented.

In some embodiments, the order of presentation of the content aggregations and/or the frequency with which one or several of the content aggregations are presented can be based on one or several attributes of the user such as, for example, one or several skills or skill levels, or the like. In some embodiments, the order of presentation can vary during the time that the content aggregations are being presented to the user, and particularly the order of presentation and/or the frequency of presentation can vary based on responses received from the user in response during the presentation of content aggregations to the user. In some embodiments, for example, the user skill and/or skill level can change based on one or several responses provided by the user, and thus the order of presentation and/or the frequency of presentation can change in response to this changed skill and/or skill level.

In some embodiments, the order and/or frequency of the presentation of the content aggregations can be controlled according to an ordering algorithm. In some embodiments, this can include, for example, a Leitner Box algorithm. In some embodiments, for example, each of the content aggregations of the presentation data can be arranged into one or several sets or groups based on, for example, a user skill or skill level relevant to that content aggregation. In some embodiments, for example, each of the content aggregations of the presentation data can be arranged into one or several sets or groups based on user response data for that content aggregation, and specifically whether the user correctly or incorrectly responded to that content aggregation and/or the frequency of user correct or incorrect responses to that content aggregation.

In some embodiments, and before a content aggregation is provided to a user, the content aggregation can be placed in one of the one or several sets based on the user skill level relevant to that content aggregation. In some embodiments, and as that content aggregation is provided to the user and responses to that content aggregation are received, the content aggregation can be moved to a different one of the one or several sets of content aggregations. In some embodiments, for example, if the user incorrectly responds to the content aggregation, then the content aggregation can be demoted to a “lower” set of content aggregations based either on the incorrect response or the decrease in the user skill level resulting from the incorrect response. Similarly, in some embodiments, for example, if the user correctly responds to the content aggregation, then the content aggregation can be promoted to a “higher” set of content aggregations based either on the correct response or the increase in the user skill level resulting from the correct response. In some embodiments, each of these sets can be associated with a frequency of presentation to the user such that “lower” sets are more frequently presented to the user and “higher” sets are less frequently presented to the user.

In some embodiments, the order and frequency algorithm can include one or several rules that can, in some circumstances limit the frequency that one or several content aggregations can be presented. In some embodiments, for example, the order and frequency algorithm can include one or several threshold values specifying a maximum allowable frequency and/or a minimum allowable amount of time between repeat presentations of a content aggregation. In some embodiments, when a content aggregation is selected for repeat presentation, the order and frequency algorithm can retrieve one or several thresholds and data identifying the frequency of presentation of the selected content aggregation and/or the time of the last presentation of the content aggregation. The order and frequency algorithm can compare the retrieved one or several thresholds and data identifying the frequency of presentation of the selected content aggregation and/or the time of the last presentation of the content aggregation to determine if the repeat presentation of the selected content aggregation is allowed. If the repeat presentation of the content aggregation is allowed, then that content aggregation can be selected for presentation.

If the repeat presentation of the content aggregation is not allowed, the order and frequency algorithm can retrieve one or several rules to select an alternative content aggregation for presentation. In some embodiments, these rules can specify the selecting of a new content aggregation for presentation from the same set of content aggregations from which the previously selected content aggregation was selected. If none of the content aggregations in the same set of content aggregations from which the previously selected content aggregation was selected are allowed for presentation, then the rules can specify the selection of a content aggregation from a “lower” set of content aggregations. These steps can be repeated until an allowable content aggregation is identified and/or until it is determined that no content aggregation is allowable for presentation. In the event that no content aggregation is allowable for presentation, then the content aggregation closest to being allowable for presentation can be selected and/or a content aggregation can be selected at random.

After the order and/or frequency of the presentation data has been determined, the process 700 proceeds to block 716, wherein the presentation data is provided to the user. In some embodiments, the presentation data can be provided to the user by the generation and sending of one or several electrical signals comprising the presentation data. In some embodiments, these one or several electrical signals can be generated by the server 102 and can be sent to the user device via, for example, the communication network 120 and the communication subsystem 532. In some embodiments, these electrical signals can be sent in the form of the communication such as an alert as discussed above. In some embodiments, the steps of blocks 714 and 716 can be performed before any content aggregation is presented to the user, and can also be performed before each content aggregation is presented to the user.

With reference now to FIG. 12, a flowchart illustrating one embodiment of a process 800 for decay based content provisioning is shown. The process 800 can be performed by all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. The process 800 begins at block 802 wherein a task is retrieved. In some embodiments, this can correspond to the step of block 706 of process 700. After the test has been retrieved, the process 800 proceeds to block 804 wherein one or several task objectives are identified. In some embodiments, these task objectives can identify one or several skills and/or skill levels desired and/or required for completion of the task. In some embodiments, these one or several skills or skill levels can be identified in the user task data and/or associated with the task retrieved in block 802.

After the task objectives have been identified, the process 800 proceeds to block 806 wherein user history is retrieved. In some embodiments, user history can be retrieved from the database server 104, and specifically from the user profile database 301. In some embodiments, user history can comprise one or several models such as, for example, a learning model and/or decay model. In some embodiments, these one or several models can date one or several user attributes such as, for example, one or several user skills and/or user skill levels.

After the user history has been retrieved, the process 800 proceeds to block 808 wherein a user mastery level is retrieved and/or determined. In some embodiments, the user history data can comprise information identifying a mastery level indicative of one or several user skills and/or user skill levels determined based on user interaction with the content distribution network 100 such as, for example, user interaction with one or several data packets, and/or information relating to the user input into the content distribution network 100. In some embodiments, determining the user mastery level can comprise extracting information identifying the mastery level from the user history data.

After the user mastery level has been determined and/or retrieved, the process 800 proceeds to block 810 wherein the user decay profile is determined. In some embodiments, the user decay profile is based on a decay model and/or is a decay model. In some embodiments, the decay model can comprise a statistical model identifying and/or predicting a rate at which one or several of the user's skills or skill levels deteriorate and/or decrease. In some embodiments, this decay model can be based on one or several user attributes that can be, for example, identified in the user history. These user attributes can include, for example, one or several preferences, learning styles, cognitive ability or intelligence levels, past skill or skill level decay, or the like.

After the decay profile has been determined, the process 800 proceeds to block 812 wherein a decay modified mastery level is determined. In some embodiments, this can include adjusting the retrieved mastery level of block 808 with the decay profile according to one or several user relevant parameters such as, for example, the amount of time between the determination of the mastery level retrieved in block 808 and the determination of the decay modified mastery level in block 812, user interactions or lack of interactions with content distribution network between the determination of the mastery level retrieved in block 808 and the determination of the decay modified mastery level in block 812, or the like. In some embodiments, the determination of the decay modified mastery level can include retrieving and/or determining these one or several parameters based on the user history retrieved in block 806, inputting these one or several parameters into the decay profile and/or decay model, inputting the retrieved mastery level into the decay profile, and outputting from the decay profile a decay modified mastery level. In some embodiments, the determination of the decay modified mastery level can be performed by the processor 102, and can be performed by the model engine 682 and/or the summary model system 404.

After the decay modified mastery level has been determined, the process 800 proceeds to block 814 wherein the processing time to reach and/or attain the task objectives identified in block 804 is determined. In some embodiments, this can include a determination of the difference between the decay modified mastery level and the one or several skills and/or skill levels embodied therein and the one or several skills or skill levels embodied in the task objectives. In some embodiments, this difference between the decay modified mastery level and the one or several skills and/or skill levels embodied in the task objectives can be input into the user model, or more specifically into the learning model. In some embodiments, the user model can output an estimated processing time for the achievement of the task objectives.

After the processing time to reach the one or several task objectives has been determined, the process 800 proceeds to block 816 wherein one or several potential content aggregations are identified. In some embodiments, the content aggregations can be generated by the user and/or can be generated by a source other than the user. In some embodiments, these one or several content aggregations can be stored in the database server 104, and particularly within the content library database 303 and/or the user profile database 301 of the database server 104. In some embodiments, these one or several content aggregations can correspond and/or relate to the one or several task objectives and/or the one or several skills and/or skill levels associated with those one or several task objectives. In some embodiments, potential content aggregations can be identified based on information associated with those one or several content aggregations and linking those one or several content aggregations with the task objectives. In some embodiments, potential content aggregations can be further identified based on, for example, data indicating the efficacy of the content aggregations, data indicating time requirements associated with the content aggregations, user feedback relating to the content aggregations, or the like.

After the potential content aggregations have been identified, the process 800 proceeds to block 818 wherein a path is mapped based on the decay modified mastery level and the task objectives. In some embodiments, the path can comprise the order presentation of identified potential content aggregations. In some embodiments, for example, the potential content aggregations can be associated with content and/or data packets which can be ordered in a hierarchical relationship. In some embodiments, the path can be mapped based on these hierarchical relationships so as to provide an ordering for delivery of the potential content aggregations corresponding to the hierarchical relationships of those content aggregations and/or of the associated related content. In some embodiments, the path can be mapped by the server 102, and specifically by recommendation engine 686 and/or the packet selection system 402.

After the path has been mapped, the process 800 proceeds to block 820 wherein one or several content aggregations are selected for delivery. In some embodiments, content aggregations selected for delivery can correspond to the first content aggregation in the map that was determined at block 818. In some embodiments, the content aggregations selected for delivery can be selected by the server 102, and specifically by the recommendation engine 686 and/or the packet selection system 402.

After the content aggregations have been selected for delivery, the process 800 proceeds to block 822 wherein the content aggregations are delivered. In some embodiments, the delivery of the content aggregation can comprise a delivery of one or several electrical signals containing data from the content aggregation to the user device 106 and the presentation of the content aggregation to the user of the user device via the I/O subsystem 526. In some embodiments, for example, this can include the delivery, either directly or indirectly, of the content aggregation from the recommendation engine 686 to the present module 672 and then to the view module 674. In some embodiments, and after the delivery of the content aggregation, the user can provide one or several responses to the content aggregation, which responses can be passed, either directly or indirectly to the response processor 678 which can evaluate the response and which evaluation can then impact one or several models relevant to either the aggregation content or the user and the providing of one or several future content aggregations.

With reference now to FIG. 13, a flowchart illustrating one embodiment of a process 900 for automated content aggregation generation is shown. The process 900 can be performed by all or components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. The process 900 begins at block 902, wherein user identification information is received. In some embodiments, the user identification information can be received from the user device 106 and/or from the supervisor device 110. In some embodiments, for example, the user identification information can be provided to the user device 106 and/or supervisor device 110 via the I/O subsystem 526 and can be communicated to the server 102 via the communications subsystem 532 and the communication network 120. In some embodiments, the user identification information can comprise a user ID, password, a username, or the like.

After the user identification information has been received, the process 900 proceeds to block 904 wherein content in the form of one or several data packets is provided to the user. In some embodiments, the one or several data packets can comprise text, images, video, audio, or the like. In some embodiments, the content can be provided to the user from the server 102 via the communication network 120, the communication subsystems 532, and the user device 106. In some embodiments, the content provided to the user can be selected by the recommendation engine 686 and can be delivered either directly or indirectly to the presentation module 672 and the view module 674.

After the content has been provided to the user, the process 900 proceeds to block 906 wherein a content selection is received. In some embodiments, for example, the user can select all or portions of the provided content via the I/O subsystem 526 of the user device 106. In some embodiments, the selection can include the highlighting of text, portions of text, images, audio content, video content, or the like. The selected content can be delivered from the I/O subsystem 526 of the user device 106 of the communication subsystem 532 of the user device 106 and then, via the communication network 120, to the server 102.

After the content selection has been received, the process 900 proceeds to block 908 wherein the content selection is parsed into sentences. In some embodiments, this parsing can be performed using one or several Natural Language Processing (NLP) techniques and/or one or several Natural Language Processing software programs running on, for example, the server 102. In some embodiments, the content selection can be parsed into sentences via the identification of periods, blank spaces, capital letters, or the like.

In some embodiments, parsing the content selection into sentences can further include a filtering of the selected content to achieve text cleaning. In some embodiments, the text cleaning can remove and/or reduce the number of unwanted artifacts in the selected content. In some embodiments, the text cleaning can include, for example, double quote normalization, hyphenated word joining, the removal of footnote references, the removal of extra white space, or the like. In some embodiments, double quote normalization can include turning left and right double quotes into standard double quotes. In some embodiments, hyphenated word joining can include identifying words hyphenated because of location at the end of the line, removal of those identified hyphens, and joining the hyphenated word. In some embodiments, the footnote references can include the identification of footnote references and the removal of the identified footnote references. In some embodiments, the removal of extra white space can include the identification of two or more consecutive whitespace characters and the replacement of those two or more consecutive whitespace characters with a single whitespace character. In some embodiments, the text cleaning can be performed before the selected content is parsed into sentences.

After the selected content has been parsed into sentences, the process 900 proceeds to block 910 where the sentences are selected. In some embodiments, this can include the selection of a previously unselected sentence. In some embodiments, for example, the selecting of a sentence can include identification of the sentences identified via parsing of the content into sentences in block 908, the identification of previously unselected sentences, and the selection of one of the previously unselected sentences. In some embodiments, a value indicative of selection is associated with a sentence when that sentence is selected.

After a sentence has been selected, the process 900 proceeds to block 912 wherein part of speech tags are generated for portions of the selected sentence. In some embodiments, the part of speech tags can identify a category or classification of one or several words or phrases in the selected speech. In some embodiments these can include, for example, classification as a noun, pronoun, adjective, determiner, verb, adverb, preposition, conjunction, interjection, or the like. In some embodiments, these can include identification of one or several noun phrases, verb phrases, adjective phrases, adverb phrases, prepositional phrases, or the like. In some embodiments, the parts of speech of the selected sentence can be identified using Natural Language Processing techniques performed by the server 102 and/or Natural Language Processing software executed by the server 102. In some embodiments, a tag can be associated with each word and/or phrase associated with a part of speech. In some embodiments, this tag can include identification of the associated word and/or phrase and data indicating the identified part of speech. These tags can be stored within the database server 104, and can be specifically stored in the content library database 303 and/or in the user profile database 301.

After the part of speech tags have been generated, the process 900 proceeds to block 914 where in a parse tree is generated for the selected sentence. The parse tree is an ordered, rooted tree that represents the syntactic structure of the selected sentence according to a context-free grammar. In some embodiments, the parse tree can be created according to one or several phrase structure grammars or one or several dependency grammars.

In some embodiments, the parse tree can comprise a root and a plurality of branches. In some embodiments, the root can identify the sentence parsed to create the parse tree, and in some embodiments, the root of the parse tree can be identified with the letter “S”. In some embodiments, each of the plurality of branches can be associated with one of the words or phrases associated with the part of speech tag. The parse tree can be generated using Natural Language Processing techniques performed by the server 102 and/or Natural Language Processing software executed by the server 102. The parse tree can be stored within the database server 104, and can be specifically stored in the content library database 303 and/or in the user profile database 301.

In some embodiments, after a parse tree is created, the parse tree can be evaluated to validate the parse tree was created for a sentence. In the event that the parse tree is not associated with a sentence such as, for example, a complete sentence, then the created parse tree is discarded. If the parse tree is discarded, then the process proceeds to block 922 and continues as will be outlined below.

After the parse tree has been created, the process 900 proceeds to block 916 wherein noun phrases in the parse tree are identified. In some embodiments, this can include the evaluation of some or all of the branches of the parse tree to determine whether each of those, some or all of the branches of the parse tree are associated with the noun phrase. The identification of the noun phrases in the parse tree can be automatically performed by the server 102 in response to the creation of a parse tree for the selected sentence.

After the noun phrases in the parse tree have been identified, the process 900 proceeds to block 918 wherein a subset of rational phrases is created. In some embodiments, the subset of rational phrases comprises a portion of the identified noun phrases selected based on one or several attributes. In some embodiments, the subset of rational phrases can be selected based on attributes thought to improve the quality of the content aggregations. In some embodiments, the subset of rational phrases can exclude noun phrases that have too many words and/or too few words. The creation of the subset of rational phrases can be automatically performed by the server 102.

After the subset of rational phrases has been created, the process 900 proceeds to block 920 wherein a content aggregation is generated. In some embodiments, the content aggregation can be generated for each of the noun phrases in the subset of rational phrases. In some embodiments, the content aggregation can be generated such that a portion of the noun phrase is identified as an extraction and a portion of the noun phrase is identified as a presentation portion. In some embodiments, the presentation portion of the content aggregation can be provided to the user and the user can respond to the presentation portion by identifying and/or attempting to identify the extraction. The content aggregation can be generated by the server 102 and the content aggregation can be stored in the database server 104, and particularly in the user profile database 301 and/or the content library database 303.

After the content aggregations been generated, the process proceeds to decision state 922 wherein it is determined if there is an additional, unselected sentence. In some embodiments, this can include determining whether any of the sentences parsed in block 908 is not associated with the value indicative of selection. If any of the parsed sentences is not associated with a value indicative of selection, then the process 900 returns to block 910 and proceeds as outlined above.

Returning again to decision state 922, if it is determined that there are no additional sentences, then the process 900 proceeds to block 924 wherein content aggregation generation is completed. In some embodiments, this can include the generation and sending of an alert to the user indicating the completion of the content aggregation for the selected content. In some embodiments, this can further include the presentation of some or all of the generated content aggregations to the user for selection, evaluation, review, or the like. In some embodiments, the completion of the generation of the content aggregation can further include the linking of the generated content aggregations to the one or several data packets from which they were generated.

With reference now to FIG. 14, a flowchart illustrating one embodiment of a process 940 for automated identification of noun phrases in a parse tree is shown. The process 940 can be performed as a part of, or in the place of the step of block 916 shown in FIG. 13. In some embodiments, the process 940 can be performed by the server 102 or by another component of the content distribution network 100. The process 940 begins at block 942 wherein the root of the parse tree is identified. In some embodiments, the root of the parse tree can be identified by searching the parse tree for the “S” indicative of the root.

After the root of the parse tree has been identified, the process 940 proceeds to block 944 and advances from the root to a branch. In some embodiments, this can include identifying all of the branches in the parse tree, identifying a subset of branches that have not been advanced to, and then advancing to one of that subset of branches. In some embodiments, a value indicating that a branch has been advanced to can be associated with a branch when that branch is advanced to. In some embodiments, this value can be stored in the same portion of the database server 104 containing the parse trees such as, for example, the user profile database 301 and/or the content library database 303.

After advancing from the root to a branch, the process 940 proceeds to block 946 wherein the part of speech tag of the advanced to branch is retrieved. In some embodiments, this can include identifying the advanced to branch, identifying the part of speech tag associated with that branch, and retrieving that part of speech tag from the database server 104, and specifically from the user profile database 301 and/or the content library database 303. After the part of speech tag has been retrieved, the process 940 proceeds to block 948 wherein the part of speech of the branch is identified. In some embodiments, this can include extracting data from the part of speech tag retrieved in block 946, and specifically can include extracting data identifying the part of speech from the part of speech tag retrieved in block 946.

After the part of speech of the branch has been identified, the process 940 proceeds to decision state 950 wherein it is determined if there is an additional, as yet un-advanced to branch. In some embodiments, this can include retrieving information identifying all the branches of the parse tree and determine whether any of the branches of the parse tree are not associated with a value indicative of having been advanced to. If some of the branches are not associated with a value indicative of having been advanced to, then the process 940 returns to block 944 and proceeds as outlined above. Alternatively if all the branches are associated with a value indicative of having been advanced to, then the process 940 advances to block 952 and proceeds to block 918 of FIG. 13.

With reference now to FIG. 15, a flowchart illustrating one embodiment of a process 960 for automated creation of a subset of rational phrases is shown. The process 960 can be performed as a part of, or in the place of the step of block 918 shown in FIG. 13. Specifically, the process 960 can be performed to create a subset of rational phrases. The process 960 begins at block 962 wherein a noun phrase is selected. In some embodiments, this can include the selection of a previously unselected noun phrase. In some embodiments, for example, the selecting of a noun phrase can include identification of the noun phrase identified in the parse tree in block 916 of process 900, the identification of previously unselected noun phrases, and the selection of one of the previously unselected noun phrases. In some embodiments, a value indicative of selection is associated with a noun phrase when that noun phrase is selected.

After the noun phrase has been selected, the process 960 proceeds to block 964, wherein the word count in the selected noun phrase is determined. In some embodiments, this can include determining, based on the parsing of the noun phrase, words contained in the noun phrase and incrementing a counter for each of the words in the noun phrase. In some embodiments, the count of words in the noun phrase can be stored in the database server 104.

After the count of words in the noun phrase is determined, the process 960 proceeds to block 966, wherein a size threshold is received and/or retrieved. In some embodiments, for example, the size threshold can comprise a single value, and in some embodiments, the size threshold can comprise a plurality of values. In some embodiments, for example, the size threshold can specify a minimum value and a maximum value. The size threshold can be, in some embodiments, received from the user device 106, and in some embodiments, the size threshold can be retrieved form the database server 104.

After the size threshold has been received/retrieved, the process 960 proceeds to block 968, wherein the word count is compared to the size threshold. In some embodiments, this can include a comparison of the word count to a single value or to a plurality of values. In embodiments in which the threshold comprises a minimum value and a maximum value, the word count can be compared to both the minimum and maximum values. After the word count has been compared to the size threshold, the process 960 proceeds to block 970, wherein a Boolean value is assigned to the selected noun phrase based on the result of the comparison. In some embodiments, for example, a first Boolean value can be assigned to the noun phrase when the noun phrase has an acceptable size in comparison to the threshold, and a second Boolean value can be assigned to the noun phrase when the noun phrase has an unacceptable size in comparison to the threshold. In some embodiments, these Boolean values can be associated with their noun phrase and can be stored in the database server 104 and particularly in the user profile database 301 and/or the content library database 303.

After the Boolean value has been assigned, the process 960 proceeds to decision state 972, wherein it is determined if there is an additional noun phrase. In some embodiments, this can include determining if there are any noun phrases that are not associated with the value indicative of selection. If it is determined that there are additional noun phrases, then the process 960 returns to block 962 and proceeds as outlined above. Alternatively, if it is determined that there are no additional, previously unselected noun phrases, then the process 960 continues to block 974 and proceeds to block 920 of FIG. 13.

With reference now to FIG. 16, a flowchart illustrating one embodiment of a detailed process 980 for automated content aggregation generation is shown. The process 980 can be performed as a part of, or in the place of the step of block 920 shown in FIG. 13. Specifically, the process 980 can be performed in generating the content aggregation. The process 980 begins at block 982, wherein a noun phrase is selected. In some embodiments, this can include the selection of a previously unselected noun phrase. In some embodiments, for example, the selecting of a noun phrase can include identification of the noun phrase identified in the parse tree in block 916 of process 900, the identification of previously unselected noun phrases, and the selection of one of the previously unselected noun phrases. In some embodiments, a value indicative of selection is associated with a noun phrase when that noun phrase is selected.

After the noun phrase has been selected, the process 980 proceeds to block 984, wherein one or several words within the noun phrase are identified as the extraction. In some embodiments, the extraction is the portion of the content aggregation that is not initially provided to the user, but that form the correct response to a presentation portion of the content aggregation. In some embodiments, the extraction portion can be selected by, for example, random selection from one or several of the words of the selected noun phrase. In some embodiments, this random selection can be made using a random number generator. In some embodiments, the extraction portion can be selected from one or several classes of words or phrases within the selected noun phrase. In some embodiments, for example, the extraction portion can be selected from one or several nouns, verbs, adjectives, adverbs, cardinal numbers, or the like. In some embodiments, the words or phrases within the selected noun phrase that can be chosen as the extraction portion are identified and the extraction portion is selected from those words or phrases. In some embodiments, the extraction portion can be identified by the server 102 and the extraction portion can be stored in the database server 104 and specifically in the user profile database 301 and/or the content library database 303.

After one or several words or phrases have been designated as the extraction, the process 980 proceeds to block 986 wherein a presentation portion is generated. In some embodiments, the presentation portion comprises a portion of the content aggregation that is initially displayed and/or provided to the user. In some embodiments, the presentation portion can be associated with a prompt to the user to provide a response to the presentation portion. In some embodiments, a desired and/or correct response to the presentation portion comprises or is the extraction portion.

In some embodiments, the generation of the presentation portion can comprise an identification of all the words in the noun phrase, the elimination of the extraction portion from all the words in the noun phrase and the insertion of placeholder markings such as, for example, underlining in the location within the noun phrase from which the extraction portion was removed. In some embodiments, presentation portion can be automatically generated by the server after the designation of the extraction portion.

After the presentation portion has been generated, the process 980 proceeds to block 988 wherein one or several content tags associated with the selected noun phrase are generated. In some embodiments, these one or several content tags can link the selected noun phrase to the data packet from which it was taken and/or to one or several topics or skills associated with the data packet from which the selected noun phrase was taken. In such an embodiment, the generation of the content tag can comprise identifying the relevant data packet and/or topics or skills within the relevant data packet and associating an indicator of the relevant data packet and/or topics or skills within the relevant data packet with the selected noun phrase.

In some embodiments, the one or several content tags can be generated based on computer analysis of the selected noun phrase. In some embodiments, this can comprise semantic analysis of all or portions of the selected noun phrase. In such an embodiment, server 102 can analyze the selected noun phrase and/or one or several words in the selected noun phrase and search the syllabus or schedule for tasks, topics, skills, and/or skill levels associated with the selected noun phrase and/or one or several words in the selected noun phrase. When tasks, topics, skills, and/or skill levels are identified, a tag comprising data identifying those tasks, topics, skills, and/or skill levels can be created and associated with the selected noun phrase and/or with one or both of the generated extraction portion and the generated presentation portion.

After the content tag has been generated, the process 980 proceeds to block 990 wherein the generated portions are combined. In some embodiments, this can include the linking and/or combination of the extraction portion, the presentation portion, and/or the content tag into a single content aggregation. In some embodiments, the content aggregation can be further combined with the selected noun phrase and/or a pointer to the selected noun phrase, and in other embodiments, the portions are combined to form a content aggregation independent and separate from the selected noun phrase.

After the portions are combined, the process 980 proceeds to block 992 wherein the content aggregation is stored. In some embodiments, the content aggregation can be stored in the database server 104 and specifically in, for example, the user profile database 301 and/or the content library database 303. In some embodiments, the content aggregation can be stored in multiple databases within the database server such as a specific user accessible database for the user creator of the content aggregation and a generally accessible database for users who did not create, or do not participate in creation of the content aggregation. In some embodiments, users may have different access rights based on the location of the content aggregation and/or users may have different access rights based on whether they were the creator of the content aggregation. In some embodiments, for example, a user creator of a content aggregation may have access rights to edit and/or modify the content aggregation whereas a non-creator user of a content aggregation may only have viewing rights to that same content aggregation.

After the content aggregation has been stored, the process 980 proceeds to block 994 wherein it is determined if there are additional phrases in the subset of rational phrases. In some embodiments, this can include determining if there are any phrases in the subset of rational phrases that are not associated with the indicator of selection. If there are any additional phrases, then the process 980 returns to block 982 and proceeds as outlined above. If there are no additional phrases, then the process 980 continues to block 996 and proceeds to block 922 of FIG. 13.

With reference now to FIG. 17, a flowchart illustrating one embodiment of a process 1000 for automated content aggregation evaluation is shown. The process 1000 can be performed by all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. The process 1000 begins at block 1002, wherein a content aggregation is retrieved. In some embodiments, the content aggregation can be the content aggregation generated according to the process of FIG. 13. The content aggregation can be retrieved from the database server and particularly from the user profile database 301 and/or the content library database 303.

After the content aggregation has been retrieved, the process 1000 proceeds to block 1004 wherein a content aggregation score is generated by, for example, the server 102. In some embodiments the source can be generated based on one or several attributes of the content aggregation such as, for example, the number of words in the content aggregation, the inclusion of one or several proper names in the content aggregation, the inclusion of one or several numbers in a content aggregation, or the like. In some embodiments, this can include, for example, identifying features of the content aggregation such as proper names and/or numbers in the extraction portion of the content aggregation, and determining attributes of those identified features such as a number of proper names and/or numbers in the extraction portion of the content aggregation. In some embodiments, a name counter can be incremented once for each proper name in the extraction portion and a number counter can be incremented once for each number in the extraction portion. In some embodiments, the attributes of the identified features, such as the number of proper names and/or numbers in the extraction portion, can be input into a scoring algorithm, which scoring algorithm can generate the content aggregation score. In some embodiments, for example, a content aggregation such as a digital flashcard can have an increased score when one or several proper names are the extraction portion and/or when the extraction portion includes one or several numbers.

After the content aggregations score has been generated, the process 1000 proceeds block 1006 wherein a score threshold is retrieved. In some embodiments, the score threshold can delineate between content aggregations having a satisfactory score and content aggregations having an unsatisfactory score. In some embodiments, the score threshold can comprise a single value, and in some embodiments the score threshold can comprise a plurality of values. The score threshold can be retrieved from the database server 104. After the score threshold has been retrieved, the process 1000 proceeds to block 1008 wherein the score generated in block 1004 is compared to the score threshold retrieved in block 1008. In some embodiments, this can include determining whether the score generated in block 1004 is greater than, less than, or equal to the score threshold.

After the score threshold has been compared to the generated score, the process 1000 proceeds to decision state 1010 wherein it is determined whether to keep the content aggregation. In some embodiments, this can include determining whether the comparison of the score threshold to the generated score indicates that content threshold is associated with an acceptable score. If it is determined to not keep the content aggregation, then the process 1000 proceeds to block 1012 wherein the content aggregation is discarded. In some embodiments, the discarding of the content aggregation can result in the deletion of the content aggregation and/or any data or files associated with the content aggregation from the database server 104.

Returning again to decision state 1010, if it is determined to keep the content aggregation, then the process 1000 proceeds block 1014 wherein the evaluation model is retrieved. In some embodiments, the evaluation model can be a statistical model configured for evaluating one or several content aggregations. In some embodiments, the statistical model can operate based on inputs comprising one or several content features and/or content parameters. As used herein, a content feature is something directly observable from the content of a content aggregation such as, for example, a count of, or an indicator of the presence or absence of one or several words, word types, phrases, or the like. As used herein, a content parameter is one or several values calculated based on one or several content features. Content parameters can include, for example, ratios, averages, or the like. In some embodiments, the evaluation model can be trained based on past or previously collected data relating to content aggregations and performances of users having used those content aggregations. The evaluation model can be retrieved from the database server 104, and specifically from the model data store 309.

After the evaluation model has been retrieved, the process 1000 proceeds to block 1016 wherein the content aggregation is parsed. In some embodiments, the content aggregation can be parsed using one or several Natural Language Processing (NLP) techniques and/or one or several Natural Language Processing software programs running on, for example, the server 102. In some embodiments, the content selection can be parsed into words, phrases, parts of speech, or the like. After the content aggregation has been parsed, the process 1000 proceeds to block 1018 wherein one or several content features are extracted from the parsed content aggregation. In some embodiments, these content features can comprise a count of the number of words in the content aggregation, a count of the number of words in the presentation portion of the content aggregation, a count of the number of words in the extraction portion of the content aggregation, a count of the number of characters in the extraction portion and/or the presentation portion, a count of the number of letters, digits, and/or numbers in the extraction portion and/or the presentation portion, the parts of speech of the words in the extraction portion, a number of capitalized letters in the extraction portion and/or the presentation portion, or the like. In some embodiments, the content features can be extracted from the parsed content aggregation by the incrementing of one or several counters based on the results of the parsing of the content aggregation by, for example, the server 102.

After the content features have been extracted, the process 1000 proceeds to block 1020 wherein one or several content parameters are generated. In some embodiments, these content parameters can be generated from the content features. These content parameters can include, for example, a ratio of the number of words in the extraction portion to the number of words in the presentation portion of the content aggregation, a ratio of the number of characters, digits, and/or letters in the extraction portion to the number of characters, digits, and/or letters in the presentation portion, an average vector space representation of blank words such as can be constructed using word embedding techniques such as Word2vec or other similar models, or the like. The content parameters can be generated by, in some embodiments, the server 102.

After the content parameters have been generated, the process 1000 proceeds to block 1022 wherein some or all of the content features and/or content parameters are input into the evaluation model. After the content features and/or content parameters are input into the evaluation model, the process 1000 proceeds to block 1024 wherein an evaluation result is generated as the output of the evaluation model. In some embodiments, the output result can be generated by the server 102.

After the evaluation result has been generated, the process 1000 proceeds to block 1026 wherein the evaluation result is compared to an evaluation threshold. In some embodiments, the evaluation threshold can comprise one or several values delineating between acceptable and unacceptable content aggregations based on the evaluation result. The evaluation threshold can be stored in the database server 104, and particularly in the threshold database 310. In some embodiments, the comparison of the evaluation result to the evaluation threshold can further include the assigning of a Boolean value to the content aggregation based on the result of the comparison. In some embodiments, for example, a first Boolean value can be assigned to the content aggregation when the content aggregation has an acceptable evaluation result in comparison to the threshold, and a second Boolean value can be assigned to the content aggregation when the content aggregation has an unacceptable evaluation result in comparison to the threshold. In some embodiments, these Boolean values can be associated with their content aggregation and can be stored in the database server 104 and particularly in the user profile database 301 and/or the content library database 303.

After the evaluation result has been compared to the evaluation threshold, the process 1000 can proceed to block 1028 wherein the content aggregation is stored or discarded based on the comparison result and/or based on the Boolean value associated with that content aggregation. In some embodiments, the storing or discarding can be performed by the server 102. In the event that a content aggregation is stored, the content aggregation can be stored in the database server 104, and specifically in the user profile database 301 and/or the content library database 303. In the event that the content aggregation is discarded, the discarding of the content aggregation can result in the deletion of the content aggregation and/or any data or files associated with the content aggregation from the database server 104.

In some embodiments, and as part of the storing or discarding of the content aggregation based on the comparison of the evaluation result and the evaluation threshold, one or several electrical signals can be generated by the server 102 and can be sent to the user device 106 and/or supervisor device 110 via, for example, the communication network 120 and the communication subsystem 532. In some embodiments, these electrical signals can be sent in the form of a communication such as an alert as discussed above.

With reference now to FIG. 18, a flowchart illustrating one embodiment of a process 1100 for automated evaluation of a set of content aggregations is shown. The process 1100 can be performed by all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. The process 1100 begins at block 1102 wherein a user identifier or user identification data is received and/or retrieved. In some embodiments, the user identifier can further include a request for evaluation of a set of content aggregations and one or several identifiers specifying the content aggregations comprising that set of content aggregations. In some embodiments, the user identifier can be received from the user device 106 and/or from the supervisor device 110. In some embodiments, for example, the user identifier can be provided to the user device 106 and/or supervisor device 110 via the I/O subsystem 526 and can be communicated to the server 102 via the communications subsystem 532 and the communication network 120. In some embodiments, the user identifier can comprise a user ID, password, a username, or the like.

After the user identification data has been received, the process 1100 proceeds to block 1104 wherein the user is identified. In some embodiments, this can include a comparison of the user identification data stored in the user profile database 301 and the user identifier or user identification data received in block 1102. After the user has been identified, the process 1100 proceeds to block 1106 wherein object network data is received and/or retrieved. In some embodiments, the step of block 1106 can comprise receiving data indicative of one or several data packets, tasks, skills, or skill levels, or the like in bodies in the object network, a syllabus, schedule, or the like. In some embodiments, the object network data can be received and/or retrieved from the database server 104 and specifically from one of the databases of the database server 104 storing the object network such as, for example, the content library database 303. In some embodiments, the object network data can characterize: the data packets in the object network; the tasks, skills, and/or skill levels associated with data packets in the object network; or the like.

After the object network data has been retrieved, the process 1100 proceeds to block 1108 wherein user aggregation data is retrieved. In some embodiments, user aggregation data can comprise data relating to the content aggregations in the user's specific database of content aggregations. In some embodiments, this aggregation data can be retrieved from the database server 104 and specifically from the content library database 303 and/or the user profile database 301.

After the user aggregation data has been retrieved, the process 1100 proceeds to block 1114 wherein the object network data and the user aggregation data are compared. In some embodiments, this comparison can include a determination of whether all of the data packets, tasks, skills, skill levels, or the like identified in the object network data are associated with content aggregations identified in the user aggregation data. In some embodiments, a first Boolean value can be associated with each data packet, task, skill, skill level, or the like that is not associated with one or several content aggregations identified in the user aggregation data and a second Boolean value can be associated with each data packet, task, skill, skill level, or the like that is associated with one or several content aggregations identified in the user aggregation data.

After the object network data and the user aggregation data have been compared, the process 1100 proceeds to block 1116 wherein an aggregation gap is identified. In some embodiments, the identification of the aggregation gaps can comprise the identification of one or several data packets, tasks, skills, skill levels, or the like that are not associated with a content aggregation identified in the user aggregation data or that are not associated with a sufficient number of content aggregations identified in the user aggregation data. In some embodiments, this determination can be made by identifying the one or several data packets, tasks, skills, skill levels, or the like that are associated with the first Boolean value.

After the aggregation gaps have been identified, the process 1100 proceeds to block 1118 wherein a gap alert is generated and sent. In some embodiments this can comprise one or several communications in the form of one or several electrical signals that identify the presence or absence of one or several aggregation gaps. In some embodiments, these communications can be generated by the server 102 and can be sent to the user device 106 and/or the supervisor device 110 as discussed above with respect to alerts.

After the generation and sending of the gap alert, the process 1100 proceeds to block 1120 wherein one or several potential aggregation gap resolutions are identified. In some embodiments, this can include, for example a searching of databases unrelated to the specific user for one or several content aggregations that could eliminate the identified aggregation gap and/or identify one or several data packets for providing to the user in connection with the prompt for the generation of additional content aggregations. In some embodiments, the server 102 can be configured to automatically review one or several other content aggregation databases for content aggregations to resolve any identified aggregation gaps.

After the potential aggregation gap resolutions have been identified, the process 1100 proceeds to block 1122 wherein one or several aggregation gaps are resolved. In some embodiments, this can include the inclusion of any identified content aggregations into the database of content aggregations associated with the specific user and/or the updating of the database of content aggregations associated with a specific user to include any newly generated content aggregations. After the aggregation gaps have been resolved, the process 1100 proceeds to block 1124 wherein a resolution report is generated and sent. In some embodiments, the resolution report can comprise a communication comprising electrical signals indicating that some or all of the identified potential aggregation gaps have been resolved and/or identifying the specific resolution of those identified potential aggregation gaps. In some embodiments, this communication can comprise an alert generated and sent as discussed above with respect to other alerts.

Referring now to FIG. 19, a flowchart illustrating one embodiment of a process 1900 for automated personalized content aggregation. In some cases it is advantageous for a user to be able to use automatically generated personalized aggregated content either in whole or in part (e.g. highlighting, selecting a timestamp, selecting a region of an image)—such as a flashcard set generated from the user's lecture notes, lecture handouts, lecture texts, online material, presentations, outlines, lecture slides, group notes, study notes, websites, etext, etext highlights, audio, video, image, websites, etc. In these circumstances the user is provided targeted aggregated content to prepare the user for a challenge such as an exam.

The process 1900 can be performed by all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. Starting at 1905, process 19 receives one or more content files from user device 106. The content files may also exist on the server 102 or data server 104 as previously uploaded by a user device 106 or they may be uploaded by a supervisor device 110 especially for the user. The content file may comprise any digital form such as PDF, word processing output, text, presentation output, digitally scanned written notes, user highlighted portions of digital text books or online content, etc.

At 1910 the process 1900 extracts the independent clauses from each content file. In some embodiments, the content file may be parsed using one or several Natural Language Processing (NLP) techniques and/or one or several Natural Language Processing software programs running on, for example, the server 102. In some embodiments, the content file may be parsed into words, phrases, parts of speech, or the like. In some embodiments the content file may be parsed into independent clauses comprising 4 words including a noun and a verb. Many methods may be used for parsing including punctuation, spaces, line changes, bullet points, etc. The method of parsing may vary greatly depending on the type of the content file being parsed. For instance, a power point slide may be parsed using bullet points on the slide. User notes may be parsed per line. Digital texts may be parsed using regular sentence spacing.

Once system 1900 has parsed the content file into independent clauses, at 1915 system 1900 extracts at least one word to form an incomplete clause at 1915. At 1917 the system 1900 associates the incomplete clause with the extracted word(s) so that the extraction is presented to the user as the proper response to the incomplete clause. At 1920 the incomplete clause is added to the aggregations file. At 1925 the extraction is added to the aggregation file. At 1930 system 1900 determines if there are more independent clauses to process in the content file and if so returns to 1910 to process the independent clauses. If there are no more independent clauses, the at 1935 the system 1900 determines if there are more content files to process. If there are more content files to process, system 1900 returns to 1905 to process the content files. At 1940 system 1900 associates the aggregation file with user of the user device 106 so that the proper file is presented to the proper user. Then at 1945 the system 1900 stores the aggregation file tin the user profile data base 301. Alternatively, the aggregation file could be stored in any data base or data store in data store servers 104. At 1950 system 1900 sends an alert to the user device 106. The alert may take on any number of forms including a notification that the aggregation file is complete for the content files sent, by the user device 106. The user device can also receive a prompt to view the aggregated content. The user device may even edit the aggregated content if the user of the user of the user device decides to do so. Alternatively, an alert or notification could be sent to a supervisor device 110 or any other device authorized to receive a notification including a parent or guardian, education administrator, counselor, etc.

Referring now to FIG. 20, a flowchart illustrating one embodiment of a process 2000 for generating multi-choice content aggregation. The process 2000 can be performed by all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. Aggregated content may be presented to the user device 106 in at least three ways. The first way may be in the form of a “confidence” presentation. A confidence presentation presents the user device 106 with an incomplete clause first and then subsequently presents the extracted word(s). The user responds to a confidence presentation with a simple “yes” or “no” indicating the user knew the extracted word or did not. Another way that aggregated content can be presented to the user device is to present an incomplete clause with two or more different extracted word phrases, wherein one of the extracted word phrases is associated with the incomplete phrase. The user can respond by selecting one of the two or more extracted phrases. Yet a third way of presenting aggregated content to the user device is to present the incomplete clause requiring the user to fill in the blank.

Process 2000 first identifies a noun phrase in the independent clause at 2005. A noun phrase is typically a word or group of words that functions in a sentence as subject, object, or prepositional object. While this description specifically discusses noun phrase extraction, the idea is not so limited and may include any type of phrase including the eight common types of word phrases: noun, verb, gerund, infinitive, appositive, participial, prepositional, and absolute. The noun phrase is extracted from the independent clause to form an incomplete clause—leaving a “blank” or empty space where the extraction occurred. Thus, the incomplete clause forms the basis of the presentation to the user device to receive a response to the empty space from the user. Once the noun phrase is extracted it is associated with the content file at 2010.

At 2015 the process 2000 stores the noun phrase in the content library store 303—or any other data store in data store server 104, The noun phrases stored in the content library store 303 will form the basis of the “test” phrases presented to the user in a multi-choice presentation. The noun phrase selected for test phrases come from the same content file such that the noun phrase will tend to be of the same subject matter as the incomplete clause from the same content file. At 2020 the process 2000 adds the incomplete clause to the aggregation file and at 2025 the process 2000 adds the noun phrase to the aggregation file. At 2030 the noun phrase and the incomplete clause are associated so as to determine which noun phrase is the correct noun phrase for the incomplete clause.

At 2040 process 2000 randomly selects a noun phrase associated with the content file from the content library store 303. Although random selection is discussed herein—any number of methods can be used to select noun phrases to become test phrase including in sequential order. Furthermore, while the only embodiment discussed herein is a selection from a noun phrases from the content file and other data base of noun phrases could be used with any manner of selecting a noun phrase for a test phrase. The randomly selected noun phrase is then a test phrase for the incomplete clause. At 2045 the test phrase is associated with the incomplete clause and is stored in the aggregation file at 2050. At 2055 system 200 determines if more test phrases are needed. The number of test phrases need is one fewer than the desired multi-choice answers that will be presented to the user—the correct noun phrase being the other. If more test phrases are required then the system 200 goes to 2040 to select more test phrases. In no more test phrases are needed then the process 2000 determines at 2060 if there are more independent clause to process. If there are process 2000 proceeds to 2005. If there are not then system 200 is finished. There are any number of modifications that may be made in process 2000. The user of the user device can be prompted to edit the independent clause, incomplete clause, the noun phrase or all of them. Or—the user may be able to modify the entire aggregated content or any part of it at any time.

Referring now to FIG. 21, a flowchart illustrating one embodiment of a process 2100 for presenting personalized content aggregation to a user device 106. The process 2100 can be performed by all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. Process 2100 receives a request for automatically generated personalized content at 2105. The request typically comes from the user device 106, but could come from a supervisor device 110 or any other device authorized to request content be presented to the user device 106. At 2110 the process 2100 transmits an incomplete clause to the user device 106 and at 2115 the process 2100 transmits one or more test phrases and/or the noun phrase to the user device. At 2120 the process 2100 receives a response from the user device 106. At 2125 process 2100 determines if the response is desirable and transmits a message to the user device 106 at 2130 indicating a desirable response.

Referring now to FIG. 22, a pictorial illustration of the movement of incomplete clauses between buckets. There are many different methods for presenting automatically personalized aggregated content to a user. In one embodiment the presentation of aggregated content is dependent on several variables including the amount of aggregated content to present, the time available to master the aggregated content, the difficulty level of the aggregated content, the optimum time for presenting at one time, etc. These variables can be adjusted such that the user reaches mastery of the aggregated content in the time period required.

FIG. 22 should be considered with Table 1 below that depicts the buckets aggregated content will be presented from. In an initial round of presentation only aggregated content in Bucket 1 will be presented. In the second round aggregated content from Bucket 1 will be presented first and then aggregated content from Bucket 2 will be presented. The aggregated content from Bucket 1 will be presented the most frequently (every round) as show in Table 1, and the aggregated content in Bucket 5 will be presented the least frequently (only in one round). At the end of 5 rounds the sequence will be repeated.

Round of presentation 1 Bucket 1 2 Bucket 1 Bucket 2 3 Bucket 1 Bucket 2 Bucket 3 4 Bucket 1 Bucket 2 Bucket 3 Bucket 4 5 Bucket 1 Bucket 2 Bucket 3 Bucket 4 Bucket 5

Initially, the least difficult aggregated content is placed in bucket 1 2305. Bucket 1 2305 is the bucket that each incomplete clause will be placed in when the user does not respond to the presentation of the incomplete clause with an undesirable answer from a presentation from any other bucket. Bucket 1 2305 is also the bucket from which incomplete clauses will be presented from the most frequently as can be determined from Table 1 above. Initially more difficult incomplete clauses will be placed in bucket 2 2310. All incomplete clauses from bucket 1 2305 with desirable responses each round will be moved from bucket 1 2305 to bucket 2 2310. Initially the hardest aggregated content will be placed in bucket 3 2315. Furthermore, all incomplete clauses associated with a desirable response from bucket 2 2310 will be moved from bucket 2 2310 to bucket 3 2315 each round. Bucket 4 2320 initially will have no aggregated content. All incomplete clauses associated with a desirable response from bucket 3 2315 each round will be moved into bucket 4 2320. Bucket 5 2325 will initially have no aggregated content. All incomplete clauses associated with a desirable response from bucket 4 2320 will be moved to bucket 5 2325 each round. All incomplete clauses associated with a desirable response from bucket 5 2325 will be moved to bucket 6 2330 each round. Once incomplete clauses are moved to bucket 6 the incomplete clause is mastered and does not need to be presented again. At each round and at each bucket, anytime the response to a presentation of an incomplete clause is undesirable that incomplete clause will be moved to bucket 1 2305. At the end of 5 rounds the sequence may be repeated. The number of incomplete clauses, the difficulty of the incomplete clauses, the total time remaining until mastery of all of the aggregated content and many other factors determine the frequency of each round. The number of rounds is also determined by the same variables. Each variable can be considered at each round based on the same variable and the mastery level of the user.

The frequency of presentation and prompting to the user to view aggregated content is determined based on numerous variables including: a progress report; the amount of time remaining until the mastery of the aggregated content is required; the number of incomplete clauses in the aggregated content; the current level of mastery of the aggregated content of the user; standard rules for optimal learning times; the elapsed time since the user of the user device was presented with aggregated content last; and the level of difficulty the user is having with the aggregated content. For instance—if an exam is in 72 hours and the user has not mastered 100 of 200 incomplete clauses—then in some embodiments the frequency might be increased from once a day to multiple times during the day. On the other hand if a user has two weeks remaining before mastery is required and has already mastered 180 of 20 incomplete clauses then in some embodiments the frequency might be decreased from once per day to once every other day. Another variable is the optimum study time wherein if a user studies more than the optimum time the studying is not beneficial. So if there are a thousand incomplete clauses and only seven days to an exam the frequency of presentation might be increased to multiple 30 minute sessions throughout each day due to the sheer volume of incomplete clauses and the small number of days left until the exam when mastery of the aggregated content is required. As such the presentation order and frequency is adaptive and responsive to variables.

Referring now to FIG. 23, a flowchart illustrating one embodiment of a process 2300 for ordering and presenting personalized aggregated content to a user. The process 2300 can be performed by all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. Process 2300 starts in this embodiment by receiving a request for aggregated content from a user device 106. In this embodiment the aggregated content takes the form of incomplete clauses but the invention is not so limited. The aggregated content could be any type of question or evaluation of any kind requiring an objective response. In response to the request the process 2300 retrieves the aggregation file associated with the user of the user device 106 making the request. The aggregation file includes incomplete clauses, associated noun phrases, and associated test phrases in this embodiment, but is not so limited and the invention contemplates many other types of content. The aggregation file may be retrieved from the user profile date store 301 or any other data store in data store server 104. The aggregation file is personalized to the user. In some embodiments the user may deliver the aggregation file to the process 2300.

Process 2300 generates three or more buckets in memory in any of the memory systems of or connected to server 102. The set of incomplete data clauses from the aggregation file are moved into each of the first, second, and third buckets at 2315. Then at 2325 each of the incomplete clause from the first bucket are tagged with a first tag. In this embodiment the first tag indicates the incomplete clause requires a confidence presentation and response. The tag remains with the incomplete clause permanently even as the incomplete clause is moved from bucket to bucket since the tag is the identifier of the type of presentation and response required. At 2330 each incomplete clause in the second bucket is tagged with a second tag. In this embodiment the second tag indicates that the incomplete clause requires a multi-choice presentation and response. Again—the second tag remains with each incomplete clause in the second bucket permanently—even as it moves from bucket to bucket. At 2335 each incomplete clause in the third bucket is tagged with a third tag. In this embodiment the third tag indicates a fill-in-the blank presentation and response is required. And this third tag also remains with each incomplete clause in the third bucket permanently.

At 2340 process 2300 presents a first incomplete clause from the first bucket to the user device followed by the noun phrase and test phrases associated with the incomplete clause as long as the incomplete clause comprises a first or second tag. Initially all incomplete clauses in the first bucket will include on a first tag. At 2345 the process 2300 will receive a response from the user device 106. At 2350 if the response from the user device is the desirable response, the incomplete clause with its tag in place is moved to the second bucket. If the response is the undesirable response the incomplete clause remains in the first bucket. At 2355 if the process 2300 determines there are more incomplete clauses in the first bucket that have not been presented, process 2300 will return to 2340. If not then at 2360 process 2300 will present an incomplete clause from the second bucket to the user device 106 along with the associated noun phrases and test phrases as required if the incomplete clause is tagged with the a first or second tag. At 2365 the process 2300 receives a response from the user device 106 and determine if the response is desirable. If so the incomplete clause is moved to the third bucket at 2370. If not the incomplete clause is moved to the first bucket at 2370. At 2375 the process 2300 determines if there are any more incomplete clauses in the second bucket. If there are process 2300 proceeds to 2360. If there are not, then process 2300 presents an incomplete clause from the third bucket to the user device 106 along with a noun phrase if it is tagged with a first or second tag and test phrases if it is tagged with a second tag. At 2385 the process 2300 receives a response from the user device and determines if it is a desirable response. At 2390 the incomplete clause is moved to a fourth bucket if there is a desirable response and to the first bucket if not. At 2393 process 2300 determines if there are more incomplete clauses in the third bucket and if so returns to 2380.

If not then process 2300 generates a progress report based on the movement of the incomplete clauses between the buckets at 2395. For instance if there are no incomplete clauses in the first bucket the user responded to each presentation with a desirable response. If the first bucket is full and the second and the third buckets are empty—the user responded undesirably to each presentation. The report may indicate that the user is mastering the aggregated content when there is less content in bucket one and more content in the third or higher numbered buckets. The report may also indicate how difficult the aggregated content is for the user to master by the same measure. The report may also be used to determining the timing of prompts to the user to view aggregated content. At 2399 the process 2300 sends the report to the user device 106.

It may also be the case that any process may recognize that the method to choose noun phrases and/or independent clauses could be adjusted to produce better aggregated content. Better aggregated content can be defined as producing a better user report, faster user mastery of the aggregated content, or any other bases for improving the aggregated content. This recognition may be accomplished by tabulating multiple user outcome and/or progress reports from one or more users and analyzing those in conjunction with the independent clauses, incomplete clauses, and/or noun phrases. It may also be the case that any process my recognize that the method of timing of prompting the user to view aggregated content can by improved using the user outcome and/or reports from one or more users. For instance—compiling all user progress reports and aggregated content mastery along with the history of aggregated content viewing may indicate that prompting should happen more or less frequently. It may also indicate that aggregated content viewing times may need to be made shorter or longer to achieve better outcomes.

Referring now to FIG. 24, a flowchart illustrating one embodiment of a process 2400 for automated personalized content aggregation generation. In some cases it is advantageous for a user to be able to use automatically generated personalized content aggregations either in whole or in part (e.g. highlighting, selecting a timestamp, selecting a region of an image)—such as a flashcard set generated from the user's lecture notes, lecture handouts, lecture texts, online material, presentations, outlines, lecture slides, group notes, study notes, websites, etext, etext highlights, audio, audio transcripts, video, image, websites, etc and/or text books. In these circumstances the user is provided targeted content aggregations to prepare the user for a challenge such as an exam.

The process 2400 can be performed by all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. Starting at 2401, process 2400 receives one or more content files or input from user device 106 or that have been otherwise uploaded to or stored on network 100 (e.g., on servers or memory). The content files as well as a user profile database as described herein may also exist on the memory, server 102 or data server 104 as previously uploaded by a user device 106 or they may be uploaded by a supervisor device 110 especially for the user. The content files may comprise any digital form such as audio files, audio transcripts, PDF, word processing output, text, text books, digitally scanned books, presentation output, digitally scanned written notes, user highlighted portions of digital text books or online content, etc.

At 2402, the system or process determines whether the content or content files provided or received are in an analyzable format (e.g., machine-readable format or medium). This determination can be made based on one or several attributes of the content files, including, for example, one or several file extensions associated with the content files, metadata associated with the content files, analysis of the content files, or the like. In one embodiment, for example, metadata of the one or several content files can be retrieved and analyzed for data indicating that some or all of the received content and/or content files are in analyzable format.

If it is determined that the received files are in analyzable format, the process proceeds to step 2406 to parse the content as described in more detail below. If the content or content files received are not in an analyzable format by the system, then the content or content files are processed into an analyzable format. In some embodiments, this processing into analyzable format can include converting all or portions of the content and/or content files into analyzable file type, the generating of new and analyzable content and/or content files from the unanalyzable content and/or content files, the extracting of all or portions of text from the content or content files such as via an Optical Character Recognition (OCR) process, or the like. The conversion of the content or content files to analyzable format can, in some embodiments, be performed by, for example, the server 102 and/or by a component or module thereof. Then process then proceeds to step 2406.

At 2406, the process 2400 parses each content file. In some embodiments, the content file(s) may be parsed using one or several Topic Modeling, Natural Language Processing (NLP) techniques (e.g., Latent Semantic Analysis, Latent Dirichlet Allocation, dependency parser or tree, neural network or representation) and/or one or several Natural Language Processing software programs running on, for example, the server 102. In some embodiments, the content file may be parsed into one or more text strings, sections, paragraphs, sentences, independent clauses, words, phrases, parts of speech, or the like. In some embodiments the content file may be parsed into independent clauses comprising 4 words including a noun and a verb. Many methods may be used for parsing including punctuation, spaces, line changes, bullet points, sections, paragraphs, etc. The method of parsing may vary greatly depending on the type of the content file being parsed. For instance, a power point slide may be parsed using bullet points on the slide. User notes may be parsed per paragraph, line or sentence. Digital texts may be parsed using regular sentence spacing, paragraphs, sections, topics, chapters, etc. Tables may be parsed per table entry.

Once system 2400 has parsed the content file(s), at 2408 system 2400 identifies or classifies a domain model associated with the parsed content file(s). For example, the system may include one or more domain models (e.g., stored on servers or memory) for different topics or subjects. These subjects may include science, history, math, language, business, accounting, geography, literature, etc. In some embodiments, a domain model can comprise a machine-learning model trained for generation and/or identification of features specific to topics and/or subjects associated with the domain model. One or several Topic Modeling, Natural Language Processing (NLP) techniques (e.g., Latent Semantic Analysis, Latent Dirichlet Allocation, dependency parser or tree, neural network or representation) and/or one or several Natural Language Processing software programs may be previously trained to each domain model (e.g., all sections or segments of each domain model). In some embodiments, the system 2400 identifies or classifies a domain model associated with each content file prior to the parsing step 2406. Domain models may be identified or classified for the content file as a whole or each parsed section or segment of a parsed content file.

At 2410, the process 2400 generates content features based on the domain model identified or associated with the content files or parsed content files in 2408. For example, the features may include one or more extracted words or combinations of words in the form of text strings (e.g. in common with, matching, similar, associated, or related to the domain model content features). Such text strings may be segments, sections, paragraphs, sentences or other independent clauses. In some embodiments, the generation of content features can include the ingestion of all or portions of the content files and/or the parsed content files into the domain model, and/or the enabling of access of the domain model to all or portions of the content files and/or the parsed content files. Then at 2412, a hierarchy of the text string or strings may be identified or determined as discussed in more detail below with respect to FIG. 25. The number of content aggregations (e.g., cards or flashcards) created or generated 2414 may be set or input by a user, determined based on size of the content file received, or proportional (e.g., equal) to a number of parsed text strings, segments, sections, paragraphs, sentences or other independent clauses of the received content file. For example, if the content file includes four paragraphs, four content aggregations, also referred to herein as content aggregation files, be may generated. As described herein, content aggregations that are generated may include or be presented as one or more text strings or clauses in the form of questions, independent clauses, noun phrases, fill in the blank clauses as described in more detail above with respect to other embodiments.

At 2416, text strings or content for each content aggregation file is selected based on the text hierarchy in 2412, and as discussed in more detail below with respect to FIG. 25. The content aggregation files are generated at 2418 and the feature vectors of the content aggregation files are generated at 2420. In some embodiments, the generation of the feature vectors for each content aggregation can include the parsing of the content aggregation according to block 2406, the applying of a domain model, such as the domain model identified in block 2408, to extract and/or identify features from the parsed content aggregation, and the creation of the feature vector from the extracted and/or identified features of the content aggregation. Content aggregation files and vectors or feature vectors are then stored at 2420 (e.g., on the servers or memory).

In some embodiments, the number of content aggregations provided or generated may also depend on previous content aggregations generated, uploaded, or stored in the memory or servers and vice versa. For example, the memory may include a first content aggregation. The first content aggregation includes at least one or more first text strings extracted from one or more first content files. The one or more first content files may be relevant or associated with a common domain model (e.g., subject or topic). In some embodiments, the one or more first content files may be sourced or received from one or more text books, online content, or other non-user generated content. The one or more first text strings may be extracted from the one or more first content files as described herein (e.g., via a topic model associated with the common domain model). Servers as described herein are configured to receive from a user or user device one or more second content files (e.g., via a communication network). In some embodiments, the one or more second content files may be sourced or received from user generated content such as lecture notes, highlights, or presentations. The one or more second content files may also be processed as described herein (e.g., according to process flows of FIGS. 24-26). One or more second text strings may then be extracted from the one or more second content files and placed in one or more second content aggregations or content aggregation files. The second content aggregation(s) or file(s) may then be associated with a user of the user device, stored in the user profile database, and transmitted to the user device via the communication network.

In some embodiments, the server may be configured to determine whether the second content aggregation is sufficient. The server may determine when or if the second content aggregation (or second content aggregations) includes a sufficient number of quality cards or files (e.g., quality or quantity) by comparing the second content aggregation to a threshold value (e.g., received from the user device or previously set) as described in more detail below with respect to FIG. 26. For example, when the second content aggregation is not sufficient (e.g., less than a threshold value), additional content aggregations may be generated or provided. In some embodiments, the first content aggregation or content aggregation files may be provided or transmitted to the user device. In other embodiments, the server determines the opposite, that is when or whether the first content aggregation (or first content aggregations) is sufficient and provides or transmits the second content aggregation when the first content aggregation is not sufficient (e.g., less than a threshold value).

In some embodiments, the first content aggregation includes two or more content aggregations or content aggregation files. The topic model associated with the common domain model may be applied to either the one or more second content files or one or more second text strings to determine whether to transfer one, two, or all of the first content aggregations. In other embodiments, a search (e.g., an elastic search) of the two or more first content aggregations may be performed by the server to determine whether to transfer one, two, or more of the first content aggregations. In such embodiments, the determination may be based on the number of words similar or overlapping in both the first content and second content aggregations. For example, if any of the first content aggregations include two or more words present in the second content aggregations, then those first content aggregations are transmitted. As described above, whether first content aggregations are generated or necessary can also depend on whether there are sufficient second content aggregations and vice versa.

Referring now to FIG. 25, a flowchart illustrating one embodiment of a process 2500 for identifying or determining a hierarchy of content segments or text strings that may be used for automated personalized content aggregation generation such as in the process 2400. The process 2500 can be performed by all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. Starting at 2501, process 2501 identifies a hierarchy of content segments or text strings of the one or more content files received from user device 106 or that have been otherwise uploaded to or stored on network 100 (e.g., on servers or memory). For example, the hierarchy of content segments or text strings of the one or more content files may include or be organized (e.g., ordered, classified, or divided) into a variety structural elements. The structural elements may include, but are not limited to: a table of contents, chapters, sections, sub-sections, paragraphs, or groups of two or more sentences or independent clauses. The structural elements of the content files may then be identified and extracted. For example, if the content file includes a plurality of structural elements in the form of paragraphs, each paragraph may be identified and extracted. The hierarchy of content segments (e.g., sub-segments or segment strata of each structural element as described in more detail below) may then be generated based on the structural elements identified and extracted. One or more content aggregations may then be generated for each structural element as described herein.

Features or feature info for each of the segments or text strings is then retrieved at 2502. Feature vectors (e.g., defined by topic modeling, natural language processing, or elastic searching as applied to or trained to each domain model) may then be generated for each of the segments or text strings at 2504. As discussed above, the feature vectors may be generated at the segment, section, paragraph, sentence and/or other independent clause level. Next at 2506 and 2508, segment strata may be identified and selected, respectively. The segment strata may be subsections, constituents of, sub-groups, or sub-segments taken or extracted from a larger segment. In some embodiments, the segment strata are text strings such as sentences or groups of words within a paragraph, section, or group of sentences (e.g., a parent section or segment). In this manner, the segment strata may have a child-parent relationship with the parent segment or paragraph. At 2510, parent segments or text strings for each of the segment strata are determined.

At 2512, feature vectors of the selected segment strata are compared to feature vectors of the parent segments that the selected segment strata are taken or extracted from. At 2514, segment strata having the closest match (e.g., with respect to the compared feature vectors) are identified. In some embodiments, this can include retrieving one or several feature vectors for the parent segments, retrieving one or several feature vectors of the selected segment strata; determining a difference between each of the one or several features vectors and the one or several parent segment feature vectors; and identifying the segment strata having the smallest difference with the one or several parent segment feature vectors. At 2516, the segment strata with the closest match are then selected for content aggregation file or card generation for the parent segment. The closest match may refer to how aligned, relevant, similar the segment strata is to the parent segment based on the feature vectors. This may be based on similar or overlapping words in the segment strata and parent segments or domain model identified. In some embodiments, only a single segment stratum is selected for content aggregation file or card generation for each parent segment. In other embodiments, more than one segment stratum (e.g., strata) is selected for content aggregation file or card generation for each parent segment.

With reference now to FIG. 26, a flowchart illustrating one embodiment of a process 2600 for automated content aggregation evaluation and presentation is shown. The process 2600 can be performed by all of the components of the content distribution network 100 including by, for example, one or several servers 102. In some embodiments, these one or several servers 102 can comprise one or several remote resources such as can occur via cloud computing or distributed processing. The process 2600 begins at block 2601, wherein domain data (e.g., a content aggregation) of one or more domain models is retrieved that are related or relevant to content files provided or input by a user or stored on network 100 (e.g., on servers or memory). In some embodiments, the domain data may be pre-selected by a user or pre-determined by the system based on the provided content files. The content files may also exist on the server 102 or data server 104 as previously uploaded by a user device 106 or they may be uploaded by a supervisor device 110 especially for the user as described herein.

As discussed above, the domain data may be related to one or more subjects or topics. In some embodiments, the domain data may include one or more content aggregation files or cards previously generated according to the processes of FIGS. 24 and 25. If or when no previous or pre-existing cards or content aggregation files are available for the retrieved domain data or model, then process 2600 at 2604 may proceed to step 2412 of process 2400 to generate cards or content aggregation files as described above with respect to the provided or received content files prior to proceeding with process 2600.

If or when previous or pre-existing cards or content aggregation files are available for the retrieved domain model within the domain data (e.g., database of all cards previously generated or pre-existing), the associated cards or content aggregation files can be retrieved (e.g., from a database server or a content library database). At 2606, card or content aggregation file metadata is retrieved. Feature vectors from the metadata may be extracted at 2608 (e.g., previously stored at 2422). The feature vectors may include extracted words or combinations of words previously extracted for the pre-existing cards or content aggregation files.

After the pre-existing cards or content aggregation files have been retrieved, the process 2600 proceeds to block 2610 wherein content features are identified for the present content files provided or input by a user or stored on network 100 (e.g., on servers or memory). Content features or feature vectors may be generated or identified as described above with respect to the process of FIG. 25. At 2612, content feature vectors are generated for the present content files. The feature vectors of the previous cards or content aggregation files (e.g., of the whole domain model or subject) are then compared to the feature vectors of the present content files at 2614.

At 2616, content aggregation match scores are generated by, for example, the server 102. In some embodiments content aggregation match scores are generated for each previous card or content aggregation file. After the content aggregation match scores have been generated, the process 2600 proceeds block 2618 wherein a score or match threshold is retrieved. In some embodiments, the match threshold can delineate between content aggregation files or cards having a satisfactory score and content aggregation files or cards having an unsatisfactory score. In some embodiments, the match threshold can comprise a single value, and in some embodiments the match threshold can comprise a plurality of values. The match threshold can be retrieved from the database server 104. After the match threshold has been retrieved, the process 2600 proceeds to block 2620 wherein the scores generated in block 2616 are compared to the match threshold retrieved in block 2618 to identify cards or content aggregation files for potential presentation or download. This provides the system or process with a discrimination feature in presenting relevant or most relevant cards or content aggregation files. In some embodiments, this can include determining whether the match score generated in block 2616 is greater than, less than, or equal to the match threshold. For example, the match threshold can delineate a quality of the match between the cards or content aggregation files and the present content files. This may, based on the comparison of the feature vectors, evaluate how closely aligned the cards or content aggregation files are to the presently provided content files. If the cards or content aggregation file scores do not meet or exceed the threshold by a certain amount, the cards may be discarded or not presented as discussed below. As discussed above, the threshold value may depend or be based upon one or more criteria, including quality or quantity.

After the match threshold has been compared to the generated score, the process 2600 proceeds to decision state 2622 wherein quality scores are determined for each card or content aggregation file based on the scores and thresholds accordingly. The cards or content aggregation files may then be ranked or ordered according to these quality scores at 2624. The cards or content aggregation files with higher quality scores are ranked higher relative to the cards or content aggregation files with lower quality scores. In this manner, cards or content aggregation files may be provided more frequently or less frequently based on the quality scores. Cards or content aggregation files may then be downloaded (e.g., to a user device) based on their quality score ranking at 2626. In some embodiments cards may be presented to a user (e.g., on a monitor or other device) based on their quality score ranking at 2628. In some embodiments, the match score or quality score may determine whether the content aggregation file or card is kept in the database or discarded. In some embodiments, this can include determining whether the quality or match score indicates that card or content aggregation file is associated with an acceptable score. If it is determined that the quality or match score is too low, the content aggregation file or card is discarded, eliminated, deleted, or not presented or available for download. In some embodiments, the discarding of the content aggregation file or card can result in the deletion of the content aggregation and/or any data or files associated with the content aggregation from the database server 104.

A number of variations and modifications of the disclosed embodiments can also be used. Specific details are given in the above description to provide a thorough understanding of the embodiments. However, it is understood that the embodiments may be practiced without these specific details. For example, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

Implementation of the techniques, blocks, steps and means described above may be done in various ways. For example, these techniques, blocks, steps and means may be implemented in hardware, software, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a swim diagram, a data flow diagram, a structure diagram, or a block diagram. Although a depiction may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in the figure(s). Additionally in combination with or instead of including additional steps not included in the figure(s), one or more steps, in whole or in part, may be removed from the process or operations depicted in the figure(s). A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software, scripting languages, firmware, middleware, microcode, hardware description languages, and/or any combination thereof. When implemented in software, firmware, middleware, scripting language, and/or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine-readable medium such as a storage medium. A code segment or machine-executable instruction may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a script, a class, or any combination of instructions, data structures, and/or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, and/or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. Any machine-readable medium tangibly embodying instructions may be used in implementing the methodologies described herein. For example, software codes may be stored in a memory. Memory may be implemented within the processor or external to the processor. As used herein, the term “memory” refers to any type of long term, short term, volatile, nonvolatile, or other storage medium and is not to be limited to any particular type of memory or number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may represent one or more memories for storing data, including read-only memory (ROM), random access memory (RAM), magnetic RAM, core memory, magnetic disk storage mediums, optical storage mediums, flash memory devices and/or other machine readable mediums for storing information. The term “machine-readable medium” includes, but is not limited to portable or fixed storage devices, optical storage devices, and/or various other storage mediums capable of storing that contain or carry instruction(s) and/or data.

While the principles of the disclosure have been described above in connection with specific apparatuses and methods, it is to be clearly understood that this description is made only by way of example and not as limitation on the scope of the disclosure. 

What is claimed is:
 1. A processor-based system for automatically generating personalized content aggregation, the processor-based system comprising: a communication network; a memory comprising a first content aggregation and at least a user profile database, the first content aggregation including one or more first text strings extracted from one or more first content files, the one or more first content files being relevant to a common subject, and the one or more first text strings selected for extraction from the one or more first content files via a topic model associated with the common subject; at least one server, comprising one or more processors, configured to: receive, from a user device, one or more second content files via a first electrical signal communicated via the communication network; process each second content file of the one or more second content files by automatically extracting one or more second text strings, from each second content file; generate a second content aggregation by placing, for each second content file, the one or more second text strings in the second content aggregation; associate the second content aggregation with a user of the user device; store the second content aggregation in the user profile database; transmit, to the user device, the second content aggregation via a second electrical signal communicated via the communication network; and transmit, to the user device, the first content aggregation via to the communication network to the user device in response to determining that the second content aggregation is not sufficient.
 2. The processor-based system for automatically generating personalized aggregated content of claim 1, the server configured to determine whether the second content aggregation meets a threshold value and to transmit, to the user device, the first content aggregation via the communication network when the second content aggregation is less than the threshold value.
 3. The processor-based system for automatically generating personalized aggregated content of claim 2, the threshold value configured to be received from the user device.
 4. The processor-based system for automatically generating personalized aggregated content of claim 1, the first content aggregation including at least two first aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and the server further configured to: apply the topic model to the one or more second text strings to determine which one of the at least two first content aggregations to transmit to the user device.
 5. The processor-based system for automatically generating personalized aggregated content of claim 1, the first content aggregation including at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and the server further configured to: apply the topic model to the one or more second content files to determine whether to transmit one or both of the at least two first content aggregations to the user device.
 6. The processor-based system for automatically generating personalized aggregated content of claim 1, the first content aggregation including at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and the server further configured to: search the first content aggregations and transmit the first content aggregations that include two or more words present in the one or more second content files.
 7. The processor-based system for automatically generating personalized aggregated content of claim 1, wherein the one or more first content files comprise written material.
 8. The processor-based system for automatically generating personalized aggregated content of claim 1, wherein the user created the one or more second content files.
 9. The processor-based system for automatically generating personalized aggregated content of claim 8, wherein the one or more content files comprise audio files, text documents, scanned documents, word processor generated documents, PDF generated documents, and/or presentation processing created documents.
 10. A method for automatically generating personalized aggregated content, the method comprising: storing in a memory a first content aggregation, the first content aggregation including one or more first text strings extracted from one or more first content files, the one or more first content files being relevant to a common subject, and the one or more first text strings selected for extraction from the one or more first content files via a topic model associated with the common subject receiving, at a server and from a user device, one or more second content files via a first electrical signal communicated via a communication network; processing each second content file of the one or more second content files by automatically extracting: one or more second text strings, from each second content file; generating a second content aggregation by placing, for each second content file, the one or more second text strings in the second content aggregation; associating the second content aggregation with a user of the user device; storing the second content aggregation in the user profile database; transmitting, to the user device, the second content aggregation via a second electrical signal communicated via the communication network; and transmitting, to the user device, the first content aggregation via the communication network in response to determining that the second content aggregation is not sufficient.
 11. The method for automatically generating personalized aggregated content of claim 10, the method further comprising determining whether the second content aggregation meets a threshold value and transmitting, to the user device, the first content aggregation via the communication network when the second content aggregation is less than the threshold value.
 12. The method for automatically generating personalized aggregated content of claim 11, wherein the threshold value configured to be received from the user device.
 13. The method for automatically generating personalized aggregated content of claim 10, wherein the first content aggregation includes at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and further comprising: applying the topic model to the one or more second text strings to determine which one of the at least two first content aggregations to transmit to the user device.
 14. The method for automatically generating personalized aggregated content of claim 10, wherein the first content aggregation includes at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and further comprising: applying the topic model to the one or more second content files to determine whether to transmit one or both of the at least two first content aggregations to the user device.
 15. The method for automatically generating personalized aggregated content of claim 10, wherein the first content aggregation includes at least two first content aggregations, each of the first content aggregations having one or more first text strings extracted from the one or more first content files, and further comprising: searching the first content aggregations and transmitting the first content aggregations that include two or more words present in the one or more second content files.
 16. The method for automatically generating personalized aggregated content of claim 10, wherein the one or more first content files comprise written material.
 17. The method for automatically generating personalized aggregated content of claim 10, wherein the user created the one or more second content files.
 18. The method for automatically generating personalized aggregated content of claim 17, wherein the one or more second content files comprise audio files, text documents, scanned documents, word processor generated documents, PDF generated documents, and/or presentation processing created documents. 